Image tampering detection and classification

using deep learning

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 Table of Content
CHAPTER TITLE PAGE NO
ABSTRACT
1. INTRODUCTION
1.1. General Introduction
1.2. Project Objectives
2. SYSTEM PROPOSAL
2.1. Existing System
2.1.1 Disadvantages
2.2. Proposed System
2.2.1 Advantages
2.3. Literature Survey
3. SYSTEM DIAGRAMS
3.1. Architecture Diagram
3.2. Flow Diagrams
3.3. Data Flow Diagram
3.4. UML Diagram
4. IMPLEMENTATION
4.1. Modules
4.2. Modules Description
5. SYSTEM REQUIREMENTS
5.1. Hardware Requirements
5.2. Software Requirements
5.3. Software Description
5.4. Testing of Products
6. CONCLUSION
7. FUTURE ENHANCEMENT
8. SAMPLE CODING
9. SAMPLE SCREENSHOT
10. REFERENCES
11. BIBILOGRAPHY

LIST OF FIGURES

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FIGURE PAGE
TITLE
NO NO
1 System Architecture

2 Flow Diagram

3 Data Flow Diagram

3.1 DFD 0

3.2 DFD 1

3.3 DFD 2

4 Use Case Diagram

5 Activity Diagram

6 Sequence Diagram

7 ER Diagram

8 Class Diagram

ABSTRACT

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This project presents a comprehensive approach to image tampering detection
and classification using deep learning techniques. The methodology involves
three primary models: a Convolutional Neural Network (CNN) based on ResNet
for binary classification of tampered versus non-tampered images, a Generative
Adversarial Network (GAN) for generating synthetic tampered images, and an
Autoencoder for detecting anomalies through reconstruction error analysis. The
ImageMaskDataset class is used to load images and their corresponding labels
by evaluating the folder structure. The dataset is split into training and testing
subsets, with data loaders facilitating efficient batching. The ResNet model is
fine-tuned for binary classification, achieving robust performance through a
standard training loop. In parallel, the GAN is designed to generate realistic
tampered images to augment the training dataset, improving the model's ability
to recognize tampering patterns. Additionally, the Autoencoder model is
implemented for unsupervised anomaly detection, utilizing reconstruction errors
to classify images as tampered or not. Evaluation metrics such as accuracy,
precision, recall, and F1-score are calculated for each model, with ROC curves
and confusion matrices visualizing performance. To enhance user experience, a
user-friendly interface is developed, enabling users to upload images for
tampering detection and providing insights into image integrity based on the
trained models. This work demonstrates the effectiveness of deep learning
methodologies in addressing the critical issue of image tampering across various
applications.

CHAPTER 1

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 INTRODUCTION

1.1 GENERAL INTRODUCTION

Image tampering detection is the process of identifying alterations or

manipulations made to digital images. With the rise of advanced editing tools,
detecting such changes has become crucial for maintaining the integrity of
digital media, especially in fields like journalism, law enforcement, and digital
forensics. This project employs deep learning techniques, including
Convolutional Neural Networks (CNNs), Generative Adversarial Networks
(GANs), and Autoencoders, to automatically detect and classify tampered
images. Using datasets like CASIA, the models are trained to identify various
tampering techniques such as copy-move and splicing. Through rigorous
evaluation using metrics like accuracy and precision, the models provide an
effective solution for tampering detection. The end goal is to develop a system
that can predict image tampering and even localize tampered areas, ensuring
reliable verification of image authenticity.

Key Characteristics:

1. Image Tampering Detection:Image tampering refers to any

manipulation or modification made to an image that alters its original
content or integrity. This includes various forms of forgery like copy-
move, splicing, image retouching, and more. Detecting such alterations is
crucial in a wide range of applications such as digital forensics, security,
legal evidence validation, and media integrity.
2. Dataset Selection: The selection of an appropriate dataset is the first and
most important step in building an image tampering detection model. A
well-known dataset for this purpose is the CASIA (Chinese Academy of
Sciences Institute of Automation) dataset. It contains tampered images
with different types of forgeries such as copy-move, splicing, and image
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 retouching. The dataset provides images with real-world tampering
examples, making it ideal for training and evaluation.

3. Real-Time Threat Detection: The system continuously monitors IoT

traffic and generates real-time alerts when suspicious or malicious
activity is detected, enabling prompt action.
4. Data-Driven Approach: The detection process relies on analyzing large
datasets of network traffic, such as the IoEd-Net dataset, that includes
both normal and malicious behavior data from IoT systems.
5. User-Friendly Dashboard: A dashboard interface provides easy access
to detection results, alert notifications, and system status, enhancing
usability for system administrators or security teams.
6. Backend Integration: The backend is implemented using Python and
Flask, with MySQL used for data storage. The system is developed using
Anaconda Navigator for streamlined development and management of
dependencies.

Workflow:

1. Data Collection: The system begins by collecting network traffic data

from IoT devices within the educational environment. This data includes
normal and malicious behavior patterns, providing the foundation for
training and evaluation.
2. Data Preprocessing: The collected data undergoes several preprocessing
steps, including handling missing values, feature engineering,
normalization, and dealing with class imbalances (e.g., by
oversampling or undersampling the dataset).
3. Model Training: The preprocessed data is split into training and test sets.
Machine learning models like CNN and KNN are trained using the

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 training set to learn patterns of benign and malicious activities in IoT
systems.
4. Model Evaluation: After training, the models are evaluated on the test
set using performance metrics such as accuracy, precision, recall, and
F1 score. Visualizations like confusion matrices and ROC curves are
used to assess model performance.
5. Detection: Once deployed, the trained model continuously monitors
network traffic. When malicious activity is detected, the system triggers
alerts, notifying administrators or users immediately.
6. Dashboard and Monitoring: The system provides a dashboard that
displays real-time threat status, alerts, and model performance, giving
users an easy way to track potential threats and system health.
7. Alert Management: Upon detecting a threat, the system generates an
alert and provides options for users to take corrective actions based on
the severity of the detected activity.

1.2 PROJECT OBJECTIVE

The objective of this project is to develop a machine learning-based malware

detection system for educational IoT environments. The system aims to
enhance the security of IoT devices in educational settings by identifying and
responding to potential security threats in real-time. The key objectives of the
project are:

1. Develop Robust Tampering Detection System

The primary goal is to create a deep learning-based system that can

accurately detect and classify tampered images, identifying various forms
of image forgery such as splicing, copy-move, and retouching.

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2. Improve Detection Accuracy

By utilizing deep learning algorithms, including CNNs, GANs, and

Autoencoders, the objective is to achieve high accuracy and precision in
detecting tampered images, ensuring reliable results in real-world
applications.

3. Create a Scalable Framework

Build a scalable and adaptable system that can process large volumes of
images and classify them in real-time, catering to different use cases like
security and image integrity checks.

4. Enhance Model Generalization

Focus on developing models that generalize well to unseen tampered

images, ensuring that the system is not overfitted and performs effectively
on diverse datasets.

5. Explore Image Forgery Types

The system should identify and classify various types of tampering such
as splicing, copy-move, and other manipulation techniques, enabling
detailed forensic analysis.

6. Generate Synthetic Data for Training

Use Generative Adversarial Networks (GANs) to augment the training

dataset by generating realistic tampered images. This will help in training
more robust models and improve detection capability.

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7. Visualize Model Performance

Provide clear visualizations of the model’s results, such as confusion

matrices, ROC curves, and examples of correctly and incorrectly
classified images, to aid in understanding model performance and areas of
improvement.

8. Develop Real-Time Image Integrity Checking Tool

Implement a user-friendly interface (e.g., using Flask) that allows end-

users to upload images and receive instant feedback on whether the image
is tampered or not, making it an accessible tool for image forensics.

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 CHAPTER 2

SYSTEM PROPOSAL

2.1 EXISTING SYSTEM

The existing systems for image tampering detection primarily rely on traditional
computer vision techniques and basic machine learning methods. These include
manual feature extraction approaches, where specific characteristics of images
like color histograms, texture analysis, and edge detection are used to detect
tampering. While effective for simpler tasks, these methods fail to adapt to
complex manipulations. Conventional machine learning algorithms, such as
Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN), are often
used on these extracted features, but they struggle with more advanced
tampering techniques and lack generalizability. Some systems also use digital
forensics, analyzing image metadata, compression artifacts, or inconsistencies
in image noise patterns to detect tampering, but they are limited to detecting
only certain types of manipulations. Recently, deep learning-based methods like
Convolutional Neural Networks (CNNs) have improved tampering detection
performance, though they still face challenges with complex or subtle
manipulations. Generative Adversarial Networks (GANs) have been explored
for augmenting training datasets by generating tampered images, but they are
computationally expensive. Additionally, anomaly detection systems, such as
autoencoders, have been used for unsupervised detection by analyzing
reconstruction errors, but these models often fail to classify the specific type of
tampering. Despite these advancements, the existing systems face several
limitations, including limited robustness, high dependency on large labeled
datasets, and inefficiency in real-time applications. These systems also struggle

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with detecting more sophisticated tampering techniques and often fail to
generalize to new types of manipulations.

2.1.1 Disadvantages:

 Limited Robustness: Traditional image tampering detection methods

struggle with complex or advanced tampering techniques. For example, subtle
manipulations like splicing, inpainting, or object removal may go undetected
due to the reliance on basic features like histograms or textures.

 High Dependency on Large Labeled Datasets: Deep learning models,

which are more effective in detecting tampering, require large labeled datasets
to train effectively. These datasets can be difficult and expensive to curate,
especially when tampered images need to be annotated.

 Inefficiency in Real-Time Applications: Many existing systems, especially

those using complex algorithms like GANs or deep neural networks, require
significant computational resources and time, making them unsuitable for real-
time tampering detection, which is often required in applications like social
media platforms or security monitoring.

 Poor Generalization: Many current systems are designed to detect specific

types of tampering. As a result, they often fail to generalize well to new or
unknown tampering techniques, limiting their applicability in diverse scenarios.

 Computational Complexity: Models like GANs and deep CNNs can be

computationally expensive to train and deploy, making them difficult to
implement on devices with limited processing power or storage.

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 Difficulty in Localizing Tampered Areas: While some systems can detect
whether an image is tampered, they often lack the ability to identify or localize
the specific areas of tampering, which is crucial in forensic investigations.

 Limited Detection Scope: Traditional methods and even some modern deep
learning approaches tend to focus only on specific types of manipulations. This
narrow detection scope means that many tampered images, especially those
involving advanced techniques or new forms of manipulation, may go
undetected.

2.1 PROPOSED SYSTEM

The proposed system for image tampering detection aims to overcome the
limitations of existing methods by leveraging advanced deep learning
techniques and more robust algorithms. The system will utilize Convolutional
Neural Networks (CNNs), specifically fine-tuned models like ResNet, for
detecting tampered images through binary classification (tampered vs. non-
tampered). Additionally, a Generative Adversarial Network (GAN) will be
employed to generate synthetic tampered images, augmenting the dataset and
enhancing the model's ability to detect a wider variety of tampering methods.
The system will also incorporate Autoencoders, which will be trained to identify
anomalies in the images by analyzing reconstruction errors, allowing for
unsupervised detection of novel tampering patterns. This multi-model approach
will not only improve the accuracy of tampering detection but also enhance the
generalization of the system to unseen tampering techniques. Furthermore, the
proposed system will offer real-time tampering detection and the ability to
localize tampered regions within the image, providing more detailed insights
into the nature of the manipulation. By integrating these advanced techniques,
the system aims to be more accurate, efficient, and adaptable across a variety of
use cases, from social media monitoring to security forensics.

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2.2.1 Advantages:

 Enhanced Accuracy: By leveraging deep learning models like CNNs,

GANs, and Autoencoders, the system can more accurately detect tampered
images, even with complex and sophisticated manipulation techniques.

 Adaptability: The system is designed to adapt to various types of tampering,

including splicing, copy-move, and retouching. The use of GANs to generate
synthetic tampered images enhances the model's robustness and allows it to
generalize better across different image manipulation methods.

 Real-Time Detection: The proposed system is capable of real-time

tampering detection, which is particularly useful for applications such as social
media monitoring, digital forensics, and security surveillance.

 Anomaly Detection: With the use of Autoencoders, the system can detect
previously unseen types of tampering by identifying anomalies based on
reconstruction errors, making it more versatile for emerging tampering methods.

 Tampering Localization: Unlike many existing systems, this approach can

not only classify an image as tampered or genuine but also localize the
tampered regions within the image, providing more detailed information about
the nature of the manipulation.

 Scalability: The deep learning models used are highly scalable, meaning
they can be trained on large datasets and handle high volumes of images
efficiently, making them suitable for deployment in large-scale environments.

 Improved Generalization: The combination of multiple deep learning

techniques allows the system to learn from diverse data patterns, improving its

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ability to generalize to unseen images and tampering techniques, thereby
reducing false positives and negatives.

 Robust Performance Across Diverse Data: The multi-model approach

ensures that the system performs well across a wide range of image types, from
low-resolution to high-resolution images, and can adapt to various real-world
scenarios, such as surveillance footage or social media images.

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2.3 LITERATURE SURVEY

1. Title: Image Tampering Detection Using Convolutional Neural Networks

(CNNs)
Year: 2019
Content Summary:

This paper explores the use of CNNs for detecting image tampering. The
authors propose a deep learning-based architecture that automatically extracts
spatial features from images to detect inconsistencies caused by tampering. The
model was trained on a dataset of tampered images and evaluated on multiple
types of image forgeries, including splicing, copy-move, and retouching.
Disadvantages:

 The method heavily relies on large labeled datasets for training, which
can be resource-intensive.
 Limited to detecting specific tampering types and struggles with complex
manipulations that involve subtle alterations.

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2. Title: Generative Adversarial Networks for Image Forgery Detection
Year: 2020

Content Summary:

This paper discusses how GANs can be applied to image tampering detection by
generating synthetic tampered images. The GAN-based approach helps augment
the training dataset, allowing the model to learn better representations of
tampered images. The authors highlight that this method can improve the
detection capabilities by teaching the model to recognize adversarial patterns
inherent in image manipulation.
Disadvantages:

 GANs require extensive computational resources for training and fine-

tuning.
 The system may not generalize well to images with novel tampering
techniques not present in the training set.

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3. Title: Autoencoder-based Anomaly Detection for Image Forgery Recognition
Year: 2021

Content Summary:

The paper presents an autoencoder-based approach for image tampering

detection. By learning the normal patterns of image data, the autoencoder can
detect anomalies when the image is tampered with. The reconstruction error of
the autoencoder is used as an indicator for detecting image tampering. The
model was shown to be effective for detecting previously unseen forgeries.
Disadvantages:

 Autoencoders can sometimes result in high false-positive rates, especially

when applied to highly noisy or complex images.
 This method is unsupervised but still requires significant validation to
ensure its performance across diverse tampering types.

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4. Title: Deep Learning Approaches for Image Tampering Detection and
Classification
Year: 2018

Content Summary:

This work explores multiple deep learning techniques, including CNNs, for the
classification of tampered images. The authors evaluate several architectures
and propose an ensemble method that combines CNNs with other machine
learning techniques to improve detection accuracy. The study also incorporates
performance metrics like accuracy, precision, and recall.
Disadvantages:

 Requires significant computational power and GPU resources, which may

not be feasible in all environments.
 The model may not perform well on low-quality or highly compressed
images, which are common in practical scenarios.

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5. Title: Tamper Detection in Digital Images Using Multi-Model Fusion
Techniques
Year: 2022

Content Summary:

This paper explores the fusion of multiple models, including CNNs, GANs, and
Autoencoders, to detect image tampering. By combining different deep learning
architectures, the system benefits from the strengths of each model, improving
overall detection accuracy. The model is evaluated on both synthetic and real-
world datasets, demonstrating its robustness across different types of forgeries.
Disadvantages:

 Multi-model fusion increases the complexity of the system and makes it

harder to optimize.
 The model’s performance may degrade when applied to real-time
applications due to the increased processing time required by combining
multiple models.

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 CHAPTER 3

SYSTEM DIAGRAM

LIST OF SYMBOLS

SYMBOL SYMBOL NAME

Use Case

Actor

Control flow

Decision Start

Start

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3.1 SYSTEM ARCHITECTURE:

FIGURE 3.1: SYSTEM ARCHITECTURE

The Architecture depicts a typical machine learning pipeline, outlining the key
stages involved in the data processing and model development workflow. It
starts with the Dataset, where the raw data is obtained, followed by Data
Loading, Data Pre-processing, and Data Splitting to prepare the data for further
analysis. The pipeline then moves to Prediction, where the machine learning
model makes predictions, Performance Metrics to evaluate the model's
performance, and the selection and implementation of Algorithms. The process
concludes with the Supervised learning approach and a final Test stage to assess
the model's performance on unseen data. This comprehensive diagram
showcases the structured and iterative nature of the machine learning lifecycle,
highlighting the interdependencies between the various components.

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3.2 FLOW DIAGRAM:

Fig 3.2 Flow Diagram

The flow diagram illustrates a machine learning pipeline that starts with Data
Loading of the "IoEd-Net: Internet of Educational Things Dataset", followed by
Null Values and Label Encoding preprocessing, Splitting the data into training,
validation, and testing sets, and then applying Convolutional Neural Networks

(CNN) and Generative Adversarial Network models. The pipeline also includes
additional Preprocessing steps, Training and Testing the models, Applying the
selected Algorithm, and concluding with Prediction With Alarm before reaching
the End Project stage. This comprehensive diagram outlines the key steps
involved in the end-to-end machine learning workflow, highlighting the
interconnected nature of the various components required for effective model
development and deployment.

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3.3 DATA FLOW DIAGRAM:

3.3.1 Level 0:

The DFD Level 0 diagram highlights two primary modules: Data Selection and
Pre-processing. In Data Selection, the ability dataset in csv format is imported
into the system.

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3.3.2 Level 1:

The DFD Level 1 depicts a machine learning pipeline that begins with a
Dataset, which is then processed through an Institutional Malware Defense
System (IMDS) component, followed by Pre-processing steps such as Label
Encoding and handling Null Values. The core of the pipeline involves
Convolutional Neural Networks (CNN) and K-Nearest Neighbors (KNN)
models, suggesting a comprehensive approach to the machine learning
workflow. The diagram illustrates the interconnected nature of the various
components required for effective model development and deployment, though
without additional context, the specific purpose or application of this pipeline
remains unclear.

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3.3.3 Level 2:

The DFD Level 2 depicts a comprehensive machine learning pipeline that

begins with Data Selection and Generation, followed by preprocessing steps
such as Label Encoding and handling Null Values. The core of the process
involves the Institutional Malware Defense System (IMDS) component, which
interacts with Convolutional Neural Networks (CNN) and K-Nearest Neighbors
(KNN) models for training and performance evaluation. The pipeline also
includes steps for Alarm Prediction, leveraging the trained models to generate
outputs. Overall, the diagram illustrates a structured approach to building and
deploying machine learning solutions, though the specific application or
purpose remains unclear without additional context.

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3.4 UML DIAGRAMS:

UML stands for Unified Modelling Language. UML is a standardized general-

purpose modelling language in the field of object-oriented software engineering.
The standard is managed, and was created by, the Object Management Group.

The goal is for UML to become a common language for creating models of
object oriented computer software. In its current form UML is comprised of two
major components: a Meta-model and a notation. In the future, some form of
method or process may also be added to; or associated with, UML.

The Unified Modelling Language is a standard language for specifying,

Visualization, Constructing and documenting the artifacts of software system,
as well as for business modelling and other non-software systems.

The UML represents a collection of best engineering practices that have proven
successful in the modelling of large and complex systems. The UML is a very
important part of developing objects oriented software and the software
development process. The UML uses mostly graphical notations to express the
design of software projects.

GOALS:

The Primary goals in the design of the UML are as follows:

1. Provide users a ready-to-use, expressive visual modelling Language so that

they can develop and exchange meaningful models.

2. Provide extendibility and specialization mechanisms to extend the core

concepts.

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 3. Be independent of particular programming languages and development
process.

4. Provide a formal basis for understanding the modelling language.

5. Encourage the growth of OO tools market.

3.4.1 USE CASE DIAGRAM:

Use-case diagrams describe the high-level functions and scope of a system.

These diagrams also identify the interactions between the system and its actors.
The use cases and actors in use-case diagrams describe what the system does
and how the actors use it, but not how the system operates internally.

A use case is a list of actions or event steps typically defining the interactions
between a role (known in the Unified Modelling Language (UML) as an actor)
and a system to achieve a goal. The actor can be a human or other external
system.

UML use case diagrams are ideal for:

 Representing the goals of system-user interactions

 Defining and organizing functional requirements in a system

 Specifying the context and requirements of a system

 Modelling the basic flow of events in a use case

Notations:

 Use cases: Horizontally shaped ovals that represent the different uses that a
user might have.

 Actors: Stick figures that represent the people actually employing the use
cases.

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 Associations: A line between actors and use cases. In complex diagrams, it
is important to know which actors are associated with which use cases.

 System boundary boxes: A box that sets a system scope to use cases. All
use cases outside the box would be considered outside the scope of that
system. For example, Psycho Killer is outside the scope of occupations in the
chainsaw example found below.

 Packages: A UML shape that allows you to put different elements into
groups. Just as with component diagrams, these groupings are represented as
file folders.

Fig 3.4.1 Use Case diagram

The above figure 3.4.1 mention that the each step in the workflow contributes to
building an effective classification model, from data collection to performance
evaluation. The use case diagram provides a high-level overview of the process,
highlighting the main tasks involved in each stage.

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3.4.2 ACTIVITY DIAGRAM:

This shows the flow of events within the system. The activities that occur within
a use case or within an objects behaviour typically occur in a sequence. An
activity diagram is designed to be simplified look at what happens during an
operations or a process. Each activity is represented by a rounded rectangle the
processing within an activity goes to compilation and then an automatic
transmission to the next activity occurs. An arrow represents the transition from
one activity to the next. An activity diagram describes a system in terms of
activities. Activities are the state that represents the execution of a set of
operations.

These are similar to flow chart diagram and dataflow.

Initial state: which state is starting the process?

Action State: An action state represents the execution of an atomic action,

typically the invocation of an operation. An action state is a simple state with an
entry action whose only exit transition is triggered by the implicit event of
completing the execution of the entry action.

Transition: A transition is a directed relationship between a source state vertex

and a target state vertex. It may be part of a compound transition, which takes
the static machine from one static configuration to another, representing the
complete response of the static machine to a particular event instance.

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Final state: A final state represents the last or "final" state of the enclosing
composite state. There may be more than one final state at any level signifying
that the composite state can end in different ways or conditions.

When a final state is reached and there are no other enclosing states it means
that the entire state machine has completed its transitions and no more
transitions can occur.

Decision: A state diagram (and by derivation an activity diagram) expresses

decision when guard conditions are used to indicate different possible
transitions that depend on Boolean conditions of the owning object.

Fig 3.4.2 Activity diagram

An activity diagram is a visual representation used in software engineering and

business process modelling to illustrate the dynamic aspects of a system. It
showcases the flow of control and data through various activities, decisions, and
concurrent processes. By using symbols like rounded rectangles for activities,
diamonds for decisions, and arrows for flow, activity diagrams help clarify
complex workflows, identify bottlenecks, and enhance understanding among
stakeholders. They are particularly useful for modelling use cases, depicting
user interactions, and analysing system behaviour.

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3.4.3 SEQUENCE DIAGRAM:

Sequence diagrams document the interactions between classes to achieve a

result, such as a use case. Because UML is designed for object-oriented
programming, these communications between classes are known as messages.
The Sequence diagram lists objects horizontally, and time vertically, and
models these messages over time.

Graphical Notation: In a Sequence diagram, classes and actors are listed as

columns, with vertical lifelines indicating the lifetime of the object over time.

Object: Objects are instances of classes, and are arranged horizontally. The
pictorial representation for an Object is a class (a rectangle) with the name
prefixed by the object.

Lifeline The Lifeline identifies the existence of the object over time. The
notation 2for a Lifeline is a vertical dotted line extending from an object.

Activation: Activations, modelled as rectangular boxes on the lifeline, indicate

when the object is performing an action.

Message: Messages, modelled as horizontal arrows between Activations.

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 Fig 3.4.3 Sequence diagram

A sequence diagram is a type of interaction diagram used in software

engineering to represent how objects communicate with one another over time.
It illustrates the sequence of messages exchanged between objects or
components in a specific scenario, detailing the order of events in a process.
Sequence diagrams use vertical lines to represent the lifelines of objects and
horizontal arrows to depict messages, including synchronous and asynchronous
calls.

3.4.4 ER DIAGRAM:

An Entity Relationship (ER) Diagram is a type of flowchart that illustrates how

“entities” such as people, objects or concepts relate to each other within a
system.

ER Diagrams are most often used to design or debug relational databases in the
fields of software engineering, business information systems, education and
research.

Also known as ERDs or ER Models, they use a defined set of symbols such as
rectangles, diamonds, ovals and connecting lines to depict the
interconnectedness of entities, relationships and their attributes.

32
They mirror grammatical structure, with entities as nouns and relationships as
verbs.

Notation:

Entity

A definable thing—such as a person, object, concept or event—that can have

data stored about it. Think of entities as nouns. Examples: a customer, student,
car or product. Typically shown as a rectangle.

Entity type: A group of definable things, such as students or athletes, whereas

the entity would be the specific student or athlete. Other examples: customers,
cars or products.

Entity set: Same as an entity type, but defined at a particular point in time, such
as students enrolled in a class on the first day.

Other examples: Customers who purchased last month, cars currently registered
in Florida. A related term is instance, in which the specific person or car would
be an instance of the entity set.

Entity categories: Entities are categorized as strong, weak or associative.

A strong entity can be defined solely by its own attributes, while a weak
entity cannot. An associative entity associates entities (or elements) within an
entity set.

Entity keys: Refers to an attribute that uniquely defines an entity in an entity

set. Entity keys can be super, candidate or primary. Super key: A set of
attributes (one or more) that together define an entity in an entity set.

Candidate key: A minimal super key, meaning it has the least possible number
of attributes to still be a super key. An entity set may have more than one

33
candidate key. Primary key: A candidate key chosen by the database designer
to uniquely identify the entity set. Foreign key: Identifies the relationship
between entities.

Relationship

How entities act upon each other or are associated with each other. Think of
relationships as verbs.

For example, the named student might register for a course.

The two entities would be the student and the course, and the relationship
depicted is the act of enrolling, connecting the two entities in that way.

Relationships are typically shown as diamonds or labels directly on the

connecting lines.

Fig 3.4.4 ER diagram

An Entity-Relationship (ER) diagram is a visual representation of the data

structures within a database, illustrating the relationships between different

34
entities. It uses symbols like rectangles to denote entities (e.g., tables),
diamonds for relationships, and ovals for attributes (fields within entities). ER
diagrams help in designing and structuring a database by defining how entities
relate to one another, such as one-to-one, one-to-many, or many-to-many
relationships. This diagram is crucial for understanding data requirements,
ensuring proper database normalization, and facilitating communication among
developers and stakeholders during the database design process.

3.3.5 CLASS DIAGRAM:

Class diagrams identify the class structure of a system, including the properties
and methods of each class. Also depicted are the various relationships that can
exist between classes, such as an inheritance relationship.

Part of the popularity of Class diagrams stems from the fact that many CASE
tools, such as Rational XDE, will auto-generate code in a variety of languages,
these tools can synchronize models and code, reducing the workload, and can
also generate Class diagrams from object-oriented code.

Graphical Notation: The elements on a Class diagram are classes and the
relationships between them.

Class: Classes are building blocks in object-oriented programming. A class is

depicted using a rectangle divided into three section.

The top section is name of class; the middle section defines the properties of
class. The bottom section list the methods of the class.

Association: An Association is a generic relationship between two classes, and

is modelled by a line connecting the two classes.

35
This line can be qualified with the type of relationship, and can also feature
multiplicity rule (e.g. one-to-one, one-to-many, many-to-many) for the
relationship.

Fi
g3.4.5 Class diagram

A class diagram is a static structure diagram in object-oriented modeling that

depicts the classes within a system and their relationships. It illustrates the
attributes (data members) and methods (functions) of each class, along with
associations, generalizations, and dependencies between classes. Using
rectangles to represent classes and lines to indicate relationships, class diagrams
provide a clear overview of the system's architecture, showcasing how different
components interact and inherit from one another. They are essential for
visualizing system design, aiding in code development, and facilitating
communication among team members in software engineering.

36
 CHAPTER 4

IMPLEMENTATION

4.1 MODULES:

• Data Selection and Loading

• Data Preprocessing

• Data Splitting

• Applying Algorithm Prediction

• Result Generation

4.2 MODULES DESCRIPTION:

4.2.1: DATA SELECTION:

• Dataset:https://www.kaggle.com/datasets/divg07/casia-20-image-
tampering-detection-dataset
• Our dataset, is in the form of .csv

4.2.2: Data Preprocessing:

1. Null Value Handling

Null values (or missing data) occur when no data is available for a particular
observation in the dataset. These missing values can arise due to various
reasons, such as errors during data collection, issues during data entry, or the
unavailability of certain information. If not addressed, null values can

37
negatively affect data analysis and machine learning models by introducing bias
or reducing the model's accuracy.

There are several strategies for handling null values:

 Removing Missing Data: You can remove rows or columns that contain
missing values. This is often done when the number of missing values is
small and does not significantly impact the overall dataset.
 Imputing Missing Data: Instead of removing data, you can fill in
missing values. This can be done using statistical methods like replacing
missing values with the mean, median, or mode of the column.
Alternatively, more advanced techniques like regression models or K-
Nearest Neighbors can be used for imputation.
 Creating a Flag: Another approach is to add a binary column indicating
whether the value was missing, which might provide useful information
for certain models.

2. Label Encoding

Label Encoding is a technique used to convert categorical values into numerical

form. It is particularly useful when the categorical variable has a natural order
or ranking (i.e., ordinal data). In this process, each unique category is assigned
an integer value.

This transformation helps machine learning algorithms process categorical data

by converting it into a format that can be understood numerically. However,
label encoding can be problematic when used on nominal data .

4.2.3: Data Splitting:

In machine learning, the dataset is often divided into two main subsets: the
training set and the test set. A common split is to allocate 80% of the data for
38
training and 20% for testing. The training set is used to teach the model,
enabling it to learn patterns and relationships between the input features and the
target variable. The model’s parameters are adjusted based on this training data
to minimize errors or loss. Using 80% for training is generally considered a
good balance, as it provides enough data for the model to learn effectively
without sacrificing too much data for evaluation.

The remaining 20% of the data is set aside for testing, providing an unbiased
evaluation of the model’s performance. The test set is only used after the model
has been trained, ensuring that it is assessed on unseen data, which simulates
real-world performance. This split helps to gauge how well the model
generalizes to new data and prevents overfitting, where a model may perform
well on the training data but poorly on unseen data. By using this 80-20 split,
the model is trained on a large enough portion of the data while still allowing
for a fair and independent evaluation of its accuracy and robustness.

4.2.4: Applying Algorithm Prediction:

Generative Adversarial Networks (GANs)

Generative Adversarial Networks (GANs) are a class of machine learning

models that consist of two neural networks — a generator and a discriminator
— which are trained simultaneously in a competitive setting. GANs are
particularly known for their ability to generate new, synthetic data that is
indistinguishable from real data, making them highly useful for image
generation, data augmentation, and even tampered image detection.

How GANs Work

1. Generator:

39
 o The generator network learns to create new data (e.g., images, text,
etc.) from random noise. It tries to produce realistic data samples
that closely resemble the real data in the training set.
o The output of the generator is initially very noisy and unrealistic,
but it improves as the network learns to fool the discriminator.

2. Discriminator:
o The discriminator is a classification network that tries to
distinguish between real data (from the training set) and fake data
(generated by the generator).
o It outputs a probability score that indicates how likely it thinks the
data is real (close to 1) or fake (close to 0).

3. Training Process:
o During training, the generator and discriminator are in a zero-sum
game where the generator tries to produce increasingly convincing
fake data, while the discriminator tries to get better at detecting
fake data.
o The generator is updated to maximize the likelihood that the
discriminator will classify its generated samples as real, and the
discriminator is updated to become better at distinguishing between
real and fake data.
o The training process continues until the generator produces data so
realistic that the discriminator cannot tell it apart from real data,
reaching a Nash equilibrium.

Convolutional Neural Networks (CNN)

Convolutional Neural Networks (CNNs) are a class of deep learning

algorithms primarily used for image processing, but they have also been

40
successfully applied to other tasks such as speech recognition, natural
language processing, and time series analysis. CNNs are particularly well-
suited for analyzing structured grid data, such as images, and they are
designed to automatically learn features from the data without the need for
manual feature extraction.

How CNN Works:

CNNs are composed of several layers that transform the input data into a
higher-level representation. The typical architecture includes the following key
layers:

1. Convolutional Layer:
o The core building block of a CNN is the convolutional layer,
which applies convolution operations to the input data using small
filters or kernels. These filters scan the input data (e.g., an image)
to detect local patterns or features such as edges, textures, or
shapes.
o The convolution operation slides a filter over the input image and
computes the dot product between the filter and the local region of
the image. This process produces a feature map (also called a
convolutional map or activation map) that highlights specific
features in the input.

2. Activation Function (ReLU):

o After the convolution operation, the output is passed through an
activation function, commonly ReLU (Rectified Linear Unit),
which introduces non-linearity. ReLU is applied element-wise and
replaces all negative values in the feature map with zero, helping
the network learn complex patterns.

41
 3. Pooling (Subsampling):
o Pooling layers are used to reduce the spatial dimensions (height
and width) of the feature maps, making the network
computationally efficient and reducing overfitting. The most
common pooling technique is max pooling, which selects the
maximum value from a small region of the feature map, thus
downsampling the output.
o Pooling layers help retain the most important information while
reducing the amount of computation required in subsequent layers.

4. Fully Connected Layer (FC):

o After multiple convolutional and pooling layers, the output is
flattened into a one-dimensional vector and passed to one or more
fully connected layers. These layers are used for decision-making
or classification. In these layers, each neuron is connected to every
neuron in the previous layer.

5. Softmax or Sigmoid Activation:

o In the final layer, a softmax activation function is typically used
for multi-class classification problems, while a sigmoid activation
is used for binary classification tasks. These functions map the
output to probabilities that represent the likelihood of each class.

Advantages of CNN:

1. Automatic Feature Extraction:

o One of the main advantages of CNNs is their ability to
automatically learn features from raw input data, removing the
need for manual feature extraction. This is particularly useful for

42
 image data, where hand-engineering features can be difficult and
time-consuming.

2. Scalability:
o CNNs are highly scalable and can handle large datasets, making
them ideal for tasks that involve large amounts of data, such as
image classification or video analysis.

3. Reduced Parameter Count:

o The use of weight sharing and local receptive fields significantly
reduces the number of parameters in the network, making CNNs
computationally efficient despite their complexity.

4. Robust to Variations:
o CNNs are robust to small changes or variations in the input, such
as translations, rotations, and scaling, thanks to pooling layers and
their hierarchical feature learning.

4.2.5 Result Generation:

Result generation in image tampering detection using deep learning models

involves evaluating the model's ability to correctly identify tampered and non-
tampered images. After training the models (e.g., Convolutional Neural
Networks, Generative Adversarial Networks, and Autoencoders), the trained
models are tested on unseen data. For CNNs, the model predicts whether an
image is tampered or not, providing a binary classification result. In the case of
GANs, the model may generate tampered images to augment the dataset or
detect tampered regions in the input image. The results are then evaluated using
key performance metrics such as accuracy, precision, recall, F1-score, and
AUC-ROC curve, which offer insights into the model’s effectiveness. Visual
tools such as confusion matrices further enhance the understanding of how well

43
the model distinguishes between tampered and untampered images.
Additionally, confidence scores can help assess the certainty of the model's
predictions. These results are critical for determining the model's performance
and its ability to generalize to new, unseen images.

CHAPTER 5

SYSTEM REQUIREMENTS

5.1 HARDWARE REQUIREMENTS:

 System : Pentium IV 2.4 GHz

 Hard Disk : 200 GB
 Mouse : Logitech.
 Keyboard : 110 keys enhanced
 Ram : 4GB

5.2 SOFTWARE REQUIREMENTS:

 O/S : Windows 10.

 Language : Python
 Front End : Flask - Framework
 Software used :Anaconda Navigator – Spyder
44
5.3 SOFTWARE DESCRIPTION:

5.3.1 Python

Python is one of those rare languages which can claim to be both simple and
powerful. You will find yourself pleasantly surprised to see how easy it is to
concentrate on the solution to the problem rather than the syntax and structure
of the language you are programming in. The official introduction to Python is
Python is an easy to learn, powerful programming language. It has efficient
high-level data structures and a simple but effective approach to object-oriented
programming. Python's elegant syntax and dynamic typing, together with its
interpreted nature, make it an ideal language for scripting and rapid application
development in many areas on most platforms. I will discuss most of these
features in more detail in the next section.

5.3.2 Features of Python

 Simple

Python is a simple and minimalistic language. Reading a good Python

program feels almost like reading English, although very strict English! This
pseudo-code nature of Python is one of its greatest strengths. It allows you to
concentrate on the solution to the problem rather than the language itself.

 Easy to Learn

As you will see, Python is extremely easy to get started with. Python has
an extraordinarily simple syntax, as already mentioned.

45
 Free and Open Source

Python is an example of a FLOSS (Free/Libré and Open Source

Software). In simple terms, you can freely distribute copies of this software,
read its source code, make changes to it, and use pieces of it in new free
programs. FLOSS is based on the concept of a community which shares
knowledge. This is one of the reasons why Python is so good - it has been
created and is constantly improved by a community who just want to see a
better Python.

 High-level Language

When you write programs in Python, you never need to bother about the
low-level details such as managing the memory used by your program, etc.

 Portable

Due to its open-source nature, Python has been ported to (i.e. changed to
make it work on) many platforms. All your Python programs can work on any
of these platforms without requiring any changes at all if you are careful enough
to avoid any system-dependent features.

You can use Python on GNU/Linux, Windows, FreeBSD, Macintosh,

Solaris, OS/2, Amiga, AROS, AS/400, BeOS, OS/390, z/OS, Palm OS, QNX,
VMS, Psion, Acorn RISC OS, VxWorks, PlayStation, Sharp Zaurus, Windows
CE and PocketPC!

You can even use a platform like Kivy to create games for your computer
and for iPhone, iPad, and Android.

 Interpreted

This requires a bit of explanation.

46
 A program written in a compiled language like C or C++ is converted
from the source language i.e. C or C++ into a language that is spoken by your
computer (binary code i.e. 0s and 1s) using a compiler with various flags and
options. When you run the program, the linker/loader software copies the
program from hard disk to memory and starts running it.

Python, on the other hand, does not need compilation to binary. You just
run the program directly from the source code. Internally, Python converts the
source code into an intermediate form called bytecodes and then translates this
into the native language of your computer and then runs it. All this, actually,
makes using Python much easier since you don't have to worry about compiling
the program, making sure that the proper libraries are linked and loaded, etc.
This also makes your Python programs much more portable, since you can just
copy your Python program onto another computer and it just works!

 Object Oriented

Python supports procedure-oriented programming as well as object-

oriented programming. In procedure-oriented languages, the program is built
around procedures or functions which are nothing but reusable pieces of
programs. In object-oriented languages, the program is built around objects
which combine data and functionality. Python has a very powerful but
simplistic way of doing OOP, especially when compared to big languages like
C++ or Java.

 Extensible

If you need a critical piece of code to run very fast or want to have some
piece of algorithm not to be open, you can code that part of your program in C
or C++ and then use it from your Python program.

47
 Embeddable

You can embed Python within your C/C++ programs to give scripting
capabilities for your program's users.

 Extensive Libraries

The Python Standard Library is huge indeed. It can help you do various
things involving regular expressions, documentation generation, unit testing,
threading, databases, web browsers, CGI, FTP, email, XML, XML-RPC,
HTML, WAV files, cryptography, GUI (graphical user interfaces), and other
system-dependent stuff. Remember, all this is always available wherever
Python is installed. This is called the Batteries Included philosophy of Python.

Besides the standard library, there are various other high-quality libraries
which you can find at the Python Package Index.

5.3.3 Flask framework:

Flask is a lightweight, Python-based web framework designed for building

web applications and APIs. It is minimalistic by default, offering only essential
features, making it highly flexible for developers to add only the components
they need. Flask uses Jinja2 templating for dynamic HTML rendering and
supports RESTful routing for API development. It includes a built-in
development server and debugger for easier testing and debugging. Flask’s
extensibility allows integration with numerous third-party libraries for
additional functionality like authentication and database handling. It is
commonly used for prototyping, microservices, and data-driven
applications.

Features of Flask:

48
1. Lightweight and Minimalistic:
o Flask is designed to be a micro-framework, meaning it provides
the basic tools needed to build a web application without imposing
any unnecessary overhead. It is simple to use and highly flexible,
allowing developers to choose the tools and libraries they want to
integrate.

2. Routing:
o Flask uses a simple URL routing mechanism to map URLs to
Python functions. It allows developers to define URL patterns and
associate them with specific functions that are triggered when a
user visits a particular URL.

3. Template Engine (Jinja2):

o Flask integrates with Jinja2, a powerful and flexible template
engine, which allows for dynamic HTML generation. This makes it
easier to render content from Python variables in the HTML page.

4. Development Server and Debugger:

o Flask comes with a built-in development server that is
lightweight, easy to run, and provides real-time feedback on code
changes. It also has an interactive debugger that helps developers
debug errors in their application during the development phase.

5. Extensible:
o While Flask itself is minimal, it supports extensions that can add
additional features like form handling, database integration (e.g.,
SQLAlchemy), authentication, file uploads, and more. Developers
can easily extend Flask by installing third-party packages.

6. RESTful Request Handling:

49
 o Flask supports RESTful routing, making it suitable for creating
REST APIs. You can handle GET, POST, PUT, DELETE, and
other HTTP methods with ease.

7. Support for Sessions and Cookies:

o Flask provides built-in support for sessions and cookies, making it
easy to manage user sessions (like login states) and store small
pieces of data on the client side.

8. Request and Response Handling:

o Flask allows developers to handle requests (e.g., form data, query
parameters) and responses (e.g., rendering HTML, JSON) in a
simple and structured way.

9. Testing Support:
o Flask has built-in support for unit testing and integrates well with
unittest or pytest. It helps developers write tests for their
application’s functionality easily.

Benefits of Flask:

1. Simplicity and Flexibility:

o Flask is known for its simplicity, making it easy to learn and use.
Developers have full control over the components they wish to use,
providing great flexibility in application design. It is a great choice
for small projects or those with simple web application needs.

2. Lightweight:
o Due to its minimalistic nature, Flask doesn’t impose any large
frameworks or tools that might be unnecessary for certain
50
 applications, leading to lower overhead. It is perfect for building
microservices or applications that need to stay lean and efficient.

3. Quick Development Cycle:

o With its development server and debugger, Flask allows developers
to quickly iterate on their code. This results in faster prototyping
and a quicker development cycle.

4. Wide Community Support:

o Flask has a large and active community that continuously
contributes to its development and provides support. The extensive
documentation and availability of third-party extensions make it
easy to extend Flask with new features.

5. Highly Extensible:
o Although Flask starts with minimal features, it is very extensible
through the use of third-party libraries. You can easily add
additional features like user authentication, databases, caching, or
form validation, allowing Flask to scale for more complex
applications.

6. Great for REST APIs:

o Flask’s RESTful routing and simplicity make it a popular choice
for building APIs. It can handle HTTP requests efficiently and is
commonly used in backend services for mobile apps, single-page
apps (SPAs), and IoT systems.

7. Microservices Friendly:

51
 o Flask’s simplicity makes it a popular choice for creating
microservices. It allows you to build independent, modular
applications that can be deployed separately, making it highly
scalable.

Typical Use Cases of Flask:

1. Web Applications:
o Flask is commonly used for building simple web applications or
full-stack applications where developers need fine-grained control
over the components. Examples include blogs, content
management systems, or small business websites.

2. RESTful APIs:
o Flask is widely used for creating RESTful APIs. Its minimalist
structure and easy handling of HTTP requests make it ideal for
backend services, where it can process client requests and serve
JSON responses. This is particularly useful in mobile apps, SPAs,
or IoT systems that need to communicate with a server.

3. Prototyping and MVPs:

o Flask is excellent for creating proof of concepts (POCs) or
Minimum Viable Products (MVPs) because of its speed in
development. Developers can quickly build and test their ideas
without the overhead of more complex frameworks like Django.

4. Microservices:
o Due to its lightweight and modular nature, Flask is frequently used
in building microservices. Each service can be developed and
deployed independently, allowing scalability and efficient
management of different parts of an application.

52
 5. Real-Time Applications:
o Flask can be used to create real-time applications, such as chat
apps or live notifications, when combined with tools like Flask-
SocketIO, which enables WebSockets for bi-directional
communication between the server and clients.

6. IoT Applications:
o Flask is often used in IoT projects where low latency and
scalability are important. It can handle API requests, manage
device data, and serve as the backend for IoT systems, processing
sensor data and responding to device queries in real-time.

7. Data Dashboards:
o Flask, along with libraries like Plotly or Matplotlib, can be used to
build data visualization dashboards for displaying analytics and
insights. These dashboards are widely used in monitoring systems,
such as IoT networks or machine learning model performance.

5.4 TESTING PRODUCTS:

System testing is the stage of implementation, which aimed at ensuring that

system works accurately and efficiently before the live operation
commence. Testing is the process of executing a program with the intent of
finding an error. A good test case is one that has a high probability of
finding an error. A successful test is one that answers a yet undiscovered .

Testing is vital to the success of the system. System testing makes a logical
assumption that if all parts of the system are correct, the goal will be
successfully achieved. . A series of tests are performed before the system is
ready for the user acceptance testing. Any engineered product can be tested

53
in one of the following ways. Knowing the specified function that a product
has been designed to from, test can be conducted to demonstrate each
function is fully operational. Knowing the internal working of a product,
tests can be conducted to ensure that “al gears mesh”, that is the internal
operation of the product performs according to the specification and all
internal components have been adequately exercised.

5.4.1 UNIT TESTING:

Unit testing is the testing of each module and the integration of the
overall system is done. Unit testing becomes verification efforts on the
smallest unit of software design in the module. This is also known as
‘module testing’.
The modules of the system are tested separately. This testing is carried out
during the programming itself. In this testing step, each model is found to be
working satisfactorily as regard to the expected output from the module.
There are some validation checks for the fields. For example, the validation
check is done for verifying the data given by the user where both format and
validity of the data entered is included. It is very easy to find error and debug
the system.

5.4.2 INTEGRATION TESTING:

Data can be lost across an interface, one module can have an adverse effect
on the other sub function, when combined, may not produce the desired
major function. Integrated testing is systematic testing that can be done with
sample data. The need for the integrated test is to find the overall
system performance. There are two types of integration testing. They are:
i)Top-down integration testing
ii) Bottom-up integration testing

54
 5.4.3 TESTING TECHNIQUES/STRATEGIES:
 WHITEBOX TESTING:
White Box testing is a test case design method that uses the control structure
of the procedural design to drive cases. Using the white box testing
methods, We Derived test cases that guarantee that all independent paths
within a module have been exercised at least once.

 BLACK BOX TESTING:

1. Black box testing is done to find incorrect or missing function

2. Interface error
3. Errors in external database access
4. Performance errors
5. Initialization and termination errors

In ‘functional testing’, is performed to validate an application conforms to

its specifications of correctly performs all its required functions. So this
testing is also called ‘black box testing’. It tests the external behaviour of
the system. Here the engineered product can be tested knowing the specified
function that a product has been designed to perform, tests can be conducted
to demonstrate that each function is fully operational.

5.4.4 SOFTWARE TESTING STRATEGIES

VALIDATION TESTING:
After the culmination of black box testing, software is completed assembly
as a package, interfacing errors have been uncovered and corrected and
final series of software validation tests begin validation testing can be
defined as many,

55
 But a single definition is that validation succeeds when the software
functions in a manner that can be reasonably expected by the customer

USER ACCEPTANCE TESTING:

User acceptance of the system is the key factor for the success of the
system. The system under consideration is tested for user acceptance by
constantly keeping in touch with prospective system at the time of
developing changes whenever required.

OUTPUT TESTING:

After performing the validation testing, the next step is output asking the
user about the format required testing of the proposed system, since no
system could be useful if it does not produce the required output in the
specific format. The output displayed or generated by the system under
consideration. Here the output format is considered in two ways. One is
screen and the other is printed format. The output format on the screen is
found to be correct as the format was designed in the system phase
according to the user needs. For the hard copy also output comes out as
the specified requirements by the user. Hence the output testing does not
result in any connection in the system.

56
5.5 TEST CASES:

1. Dataset Loading Test

 Test Case 1.1: Valid Dataset

o Description: Test if the ImageMaskDataset class correctly loads
images and labels from a directory with the expected folder
structure.
o Input: Folder structure with two subdirectories: tampered and
non_tampered, each containing relevant images.
o Expected Output: Dataset should load correctly with images and
labels corresponding to the folder names.

 Test Case 1.2: Invalid Dataset Structure

o Description: Test if the ImageMaskDataset class handles an
invalid folder structure (missing subdirectories or improperly
named directories).
o Input: Folder structure with missing or incorrectly named
subdirectories.
o Expected Output: Error or warning indicating the dataset structure
is not as expected.

2. ResNet Model Test

 Test Case 2.1: Model Training

o Description: Test if the ResNet model can be trained on a valid
dataset.
o Input: Training dataset with tampered and non_tampered images.
o Expected Output: The model trains successfully without errors,
and metrics (accuracy, precision, recall, F1-score) are outputted.

57
  Test Case 2.2: Model Performance Evaluation
o Description: Test if the trained ResNet model performs correctly
on unseen test data.
o Input: A separate test set with labeled images.
o Expected Output: Evaluation metrics (accuracy, precision, recall,
F1-score) should be calculated correctly, and ROC curve should be
plotted.

 Test Case 2.3: Model Inference

o Description: Test the inference capabilities of the ResNet model
on a single input image.
o Input: A single test image, either tampered or non-tampered.
o Expected Output: Model should predict either 'tampered' or 'non-
tampered' along with the associated confidence score.

3. GAN Model Test

 Test Case 3.1: GAN Training

o Description: Test if the GAN model can generate synthetic
tampered images.
o Input: A set of non-tampered images for the GAN to generate
tampered counterparts.
o Expected Output: The GAN should generate realistic tampered
images.

 Test Case 3.2: Image Augmentation

o Description: Test if the synthetic tampered images from the GAN
can be added to the training dataset.
o Input: A batch of generated tampered images and the original
training dataset.

58
 o Expected Output: The training dataset should be successfully
augmented with new tampered images.

4. Autoencoder Model Test

 Test Case 4.1: Autoencoder Training

o Description: Test if the autoencoder model can be trained to learn
the distribution of non-tampered images.
o Input: Non-tampered images for training the autoencoder.
o Expected Output: The autoencoder should learn to reconstruct
non-tampered images with low error.

 Test Case 4.2: Anomaly Detection

o Description: Test if the autoencoder can detect anomalies (i.e.,
tampered images) based on reconstruction error.
o Input: A set of tampered and non-tampered test images.
o Expected Output: The autoencoder should generate higher
reconstruction errors for tampered images compared to non-
tampered images, thus classifying them correctly.

5. Evaluation Metrics Test

 Test Case 5.1: Accuracy Calculation

o Description: Test if the accuracy of each model (ResNet, GAN,
Autoencoder) is calculated correctly during evaluation.
o Input: A test set with known ground truth labels.
o Expected Output: The model should output a calculated accuracy
value that matches the correct classification of tampered and non-
tampered images.

 Test Case 5.2: Confusion Matrix Generation

59
 o Description: Test if the confusion matrix is generated correctly
after evaluation.
o Input: A test set with labeled tampered and non-tampered images.
o Expected Output: The confusion matrix should reflect the true
positives, true negatives, false positives, and false negatives.

 Test Case 5.3: ROC Curve

o Description: Test if the ROC curve is plotted correctly during
evaluation.
o Input: A test set with labeled tampered and non-tampered images.
o Expected Output: A valid ROC curve showing the trade-off
between true positive rate and false positive rate.

6. User Interface Test

 Test Case 6.1: Image Upload and Prediction

o Description: Test the functionality of the user interface where
users upload an image for tampering detection.
o Input: A user-uploaded image, either tampered or non-tampered.
o Expected Output: The model should predict whether the image is
tampered or not and provide the user with the corresponding result.

 Test Case 6.2: Invalid Image Format

o Description: Test if the interface handles invalid image formats.
o Input: An unsupported image format (e.g., non-JPEG, non-PNG).
o Expected Output: An error message indicating that the image
format is unsupported.

7. Performance and Scalability Test

 Test Case 7.1: Model Inference Speed

60
 o Description: Test the speed of model inference (i.e., time taken for
predictions on a single image).
o Input: A single tampered or non-tampered image.
o Expected Output: The prediction should be made within a
reasonable time (e.g., <1 second).

 Test Case 7.2: Batch Processing of Multiple Images

o Description: Test the system's ability to process multiple images in
batch mode.
o Input: A batch of images.
o Expected Output: The system should process the batch and return
predictions for all images.

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 CHAPTER 6

CONCLUSION

In conclusion, the application of deep learning techniques for image tampering

detection and classification demonstrates a significant advancement in ensuring
the authenticity and integrity of digital media. By utilizing models like
Convolutional Neural Networks (CNNs), Generative Adversarial Networks
(GANs), and Autoencoders, it is possible to efficiently detect tampered images
and classify them based on various forgeries such as copy-move, splicing, and
retouching. CNNs, with their ability to capture intricate spatial features, are
well-suited for identifying patterns indicative of image manipulation, while
GANs are utilized to generate synthetic tampered images, enriching the training
dataset and enabling the detection model to recognize subtle forms of
tampering. Autoencoders, on the other hand, excel at anomaly detection by
comparing reconstructed images to original inputs, allowing for the
identification of discrepancies that signal tampering.

The pre-processing steps, such as resizing, normalization, and augmentation,

help standardize and expand the dataset, making it more adaptable to the
varying characteristics of real-world images. Noise removal ensures that the
model focuses on relevant features without being distracted by irrelevant or
harmful distortions. The combination of these steps enables the models to
achieve higher generalization and robustness when applied to new, unseen data.

To evaluate the model's performance, metrics such as accuracy, precision,

recall, F1-score, and Area Under the ROC Curve (AUC-ROC) offer a

62
quantitative and qualitative understanding of how well the system distinguishes
between tampered and genuine images. These metrics help fine-tune the models
and ensure that they are not only accurate but also reliable in terms of
identifying both false positives and false negatives. Additionally, the ability to
visualize tampered regions using attention mechanisms and segmentation
techniques further enhances the system's interpretability, providing clear
evidence of tampering for users.

While these techniques show great promise, further advancements in the

refinement of models, fine-tuning of parameters, and expansion of datasets are
necessary to handle more complex tampering methods and to increase the
system's performance in real-world applications. As the field of image
tampering detection continues to evolve, the integration of these deep learning-
based methods into various industries such as forensics, security, and media
verification will play a crucial role in combating the spread of manipulated
images and ensuring digital content integrity. The potential to apply these
solutions across diverse domains, ranging from social media platforms to legal
investigations, highlights the importance of ongoing research and development
in the fight against image forgeries.

63
 CHAPTER 7

FUTURE ENHANCEMENT

Future enhancements in image tampering detection and classification using

deep learning hold great potential for addressing current limitations and further
improving the accuracy, efficiency, and robustness of these systems. One key
area of improvement lies in the development of more sophisticated models that
can detect even more advanced tampering techniques, such as deepfake
technology and subtle image manipulations that are not easily detectable by
conventional methods. This can be achieved by incorporating more advanced
neural network architectures, such as transformer-based models, which have
shown promise in understanding complex patterns in data.

Additionally, expanding and diversifying the datasets used for training deep
learning models will be crucial for better generalization. Currently, datasets like
CASIA provide valuable resources, but there is a need for more extensive and
varied datasets that include a wider range of image types, tampering techniques,
and real-world scenarios. By training models on larger and more diverse
datasets, the system can become more adaptable to various types of image
manipulation, ensuring it can handle new challenges in image forensics.

Improving real-time performance is another important aspect for future

development. While current systems are effective, there is still room for
optimization to ensure faster detection times, especially for applications in areas
such as security and media verification. Techniques such as model quantization,

64
pruning, and hardware acceleration can be employed to make these models
more efficient, reducing inference time while maintaining high accuracy.

Moreover, the integration of multi-modal data, such as combining image

analysis with metadata (e.g., EXIF data, timestamps), can provide additional
context for tampering detection. This could help in verifying the authenticity of
images by cross-referencing them with other available data sources, adding
another layer of validation.

Lastly, as the field of explainable AI (XAI) continues to evolve, enhancing the

transparency of deep learning models will be crucial for building trust and
understanding in image tampering detection systems. By developing techniques
that allow for better interpretability of model decisions, users will be able to
gain insights into why a particular image was classified as tampered or
authentic, leading to more informed decisions in various real-world
applications.

Overall, these future enhancements will contribute to creating more accurate,

efficient, and reliable image tampering detection systems, making them a
valuable tool for digital forensics, media integrity verification, and security
across numerous industries.

65
 CHAPTER 8

SAMPLE CODING

###################Image tampering detection and classification using deep

learning#########################################

import os

import torch

import shutil

import random

from PIL import Image

from torch.utils.data import Dataset, DataLoader, random_split

from torchvision import transforms, models

import torch.nn as nn

import torch.optim as optim

from tqdm import tqdm # For progress bars during training

# Custom Dataset Class to load images and generate labels

class ImageMaskDataset(Dataset):

def __init__(self, image_folder, mask_folder, transform=None):

66
 """

Args:

image_folder (str): Path to the folder containing the images.

mask_folder (str): Path to the folder containing the corresponding

masks.

transform (callable, optional): A function/transform to apply to the

images.

"""

self.image_folder = image_folder

self.mask_folder = mask_folder

self.transform = transform

# List all image files (assuming the files in both IMAGE and MASK are
identical)

self.image_files = sorted(os.listdir(image_folder))

def __len__(self):

return len(self.image_files)

def __getitem__(self, idx):

# Get the image file name

image_name = self.image_files[idx]

image_path = os.path.join(self.image_folder, image_name)

67
 # Open the image

image = Image.open(image_path).convert("RGB") # Convert to RGB if

not already

# Determine the class based on folder structure

# Let's assume the images from 'tampered' are the positive class (1), and
'not_tampered' is the negative class (0)

if "tampered" in image_path:

label = 1

else:

label = 0

# Apply transformations if provided

if self.transform:

image = self.transform(image)

return image, label

# Define the transformations (Resize, Normalize, etc.)

transform = transforms.Compose([

transforms.Resize((256, 256)), # Resize images and masks to 256x256

transforms.ToTensor(), # Convert image to tensor

68
 transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
# Standard normalization for ResNet

])

# Path to your IMAGE and MASK folders

image_folder = 'TEST/TRAINING_CG-1050/TRAINING/ORIGINAL'

mask_folder = 'TEST/TRAINING_CG-1050/TRAINING/TAMPERED' # Not

actually used for labels in classification task

# Create the dataset

dataset = ImageMaskDataset(image_folder=image_folder,
mask_folder=mask_folder, transform=transform)

# Split dataset into training and test sets (80% for training, 20% for testing)

train_size = int(0.8 * len(dataset)) # 80% for training

test_size = len(dataset) - train_size # 20% for testing

train_dataset, test_dataset = random_split(dataset, [train_size, test_size])

# Create DataLoader for batching

batch_size = 8

train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)

69
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

# Check the dataset loading by printing the shape of the first batch

for images, labels in train_loader:

print(f"Images batch shape: {images.shape}")

print(f"Labels batch shape: {labels.shape}")

break # Check the first batch only

###########################ResNet model-CNN
####################################

# 2. Model definition and training loop

# Set device (use GPU if available)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load pre-trained ResNet model

model = models.resnet18(pretrained=True) # Use ResNet-18 (you can change it

to ResNet-50 or ResNet-101)

model.fc = nn.Linear(model.fc.in_features, 2) # Modify the last fully connected

layer for binary classification

# Move model to device (GPU or CPU)

70
model = model.to(device)

# Loss function and optimizer

criterion = nn.CrossEntropyLoss() # Loss for classification

optimizer = optim.Adam(model.parameters(), lr=0.001) # Adam optimizer

# Training loop

num_epochs = 10 # Set the number of epochs

for epoch in range(num_epochs):

model.train() # Set model to training mode

running_loss = 0.0

correct_preds = 0

total_preds = 0

# Loop over batches in the training set

for images, labels in tqdm(train_loader, desc=f"Epoch

{epoch+1}/{num_epochs}", unit="batch"):

images, labels = images.to(device), labels.to(device)

# Zero the parameter gradients

optimizer.zero_grad()

71
 # Forward pass

outputs = model(images)

loss = criterion(outputs, labels)

# Backward pass and optimization

loss.backward()

optimizer.step()

# Track statistics

running_loss += loss.item()

_, predicted = torch.max(outputs, 1)

correct_preds += (predicted == labels).sum().item()

total_preds += labels.size(0)

# Calculate training accuracy

train_accuracy = (correct_preds / total_preds) * 100

print(f"Epoch {epoch+1}/{num_epochs} - Loss:

{running_loss/len(train_loader):.4f} - Accuracy: {train_accuracy:.2f}%")

# 3. Testing the model

model.eval() # Set model to evaluation mode

correct_preds = 0

72
total_preds = 0

with torch.no_grad(): # No need to track gradients during evaluation

for images, labels in tqdm(test_loader, desc="Testing", unit="batch"):

images, labels = images.to(device), labels.to(device)

# Forward pass

outputs = model(images)

_, predicted = torch.max(outputs, 1)

# Track statistics

correct_preds += (predicted == labels).sum().item()

total_preds += labels.size(0)

# Calculate test accuracy

test_accuracy = (correct_preds / total_preds) * 100

print(f"Test Accuracy: {test_accuracy:.2f}%")

################################################################
###############

import torch

import torch.nn as nn

73
import torch.optim as optim

import os

from torch.utils.data import DataLoader

from torchvision import transforms

from torchvision.datasets import ImageFolder

from torchvision.utils import save_image

from torch import nn

# Set device to CUDA if available, otherwise fallback to CPU

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Define the Generator

class Generator(nn.Module):

def __init__(self, z_dim=100, channels_img=3, feature_map_size=64):

super(Generator, self).__init__()

self.gen = nn.Sequential(

nn.ConvTranspose2d(z_dim, feature_map_size * 8, 4, 1, 0, bias=False),

nn.BatchNorm2d(feature_map_size * 8),

nn.ReLU(True),

nn.ConvTranspose2d(feature_map_size * 8, feature_map_size * 4, 4, 2,
1, bias=False),

74
 nn.BatchNorm2d(feature_map_size * 4),

nn.ReLU(True),

nn.ConvTranspose2d(feature_map_size * 4, feature_map_size * 2, 4, 2,
1, bias=False),

nn.BatchNorm2d(feature_map_size * 2),

nn.ReLU(True),

nn.ConvTranspose2d(feature_map_size * 2, feature_map_size, 4, 2, 1,
bias=False),

nn.BatchNorm2d(feature_map_size),

nn.ReLU(True),

nn.ConvTranspose2d(feature_map_size, channels_img, 4, 2, 1,
bias=False),

nn.Tanh()

def forward(self, x):

return self.gen(x)

# Define the Discriminator

class Discriminator(nn.Module):

75
 def __init__(self, channels_img=3, feature_map_size=64):

super(Discriminator, self).__init__()

self.disc = nn.Sequential(

nn.Conv2d(channels_img, feature_map_size, 4, 2, 1, bias=False),

nn.LeakyReLU(0.2, inplace=True),

nn.Conv2d(feature_map_size, feature_map_size * 2, 4, 2, 1, bias=False),

nn.BatchNorm2d(feature_map_size * 2),

nn.LeakyReLU(0.2, inplace=True),

nn.Conv2d(feature_map_size * 2, feature_map_size * 4, 4, 2, 1,
bias=False),

nn.BatchNorm2d(feature_map_size * 4),

nn.LeakyReLU(0.2, inplace=True),

nn.Conv2d(feature_map_size * 4, feature_map_size * 8, 4, 2, 1,
bias=False),

nn.BatchNorm2d(feature_map_size * 8),

nn.LeakyReLU(0.2, inplace=True),

nn.Conv2d(feature_map_size * 8, 1, 4, 1, 0, bias=False)

76
 def forward(self, x):

return self.disc(x)

####################### Training Loop for the GAN

##############################

# Hyperparameters

z_dim = 100 # Latent vector size (input to the generator)

lr = 0.0002 # Learning rate

batch_size = 64

epochs = 50

img_size = 256 # Image size for training (resize the images)

channels_img = 3 # RGB images

feature_map_size = 64 # Number of feature maps in each convolution layer

# Set up data transforms for normalization

transform = transforms.Compose([

transforms.Resize(img_size),

transforms.CenterCrop(img_size),

transforms.ToTensor(),

transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), # Normalize

to [-1, 1]

])

77
# Load the dataset (images are from the 'tampered' folder for GAN training)

dataset = ImageFolder(root="TEST/TRAINING_CG-1050/TRAINING",
transform=transform)

dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)

# Initialize the models

generator = Generator(z_dim=z_dim, channels_img=channels_img,

feature_map_size=feature_map_size).to(device)

discriminator = Discriminator(channels_img=channels_img,
feature_map_size=feature_map_size).to(device)

# Loss function and optimizers

criterion = nn.BCEWithLogitsLoss() # Binary Cross-Entropy loss

optimizer_gen = optim.Adam(generator.parameters(), lr=lr, betas=(0.5, 0.999))

optimizer_disc = optim.Adam(discriminator.parameters(), lr=lr, betas=(0.5,

0.999))

# Create directory for saving images if it doesn't exist

os.makedirs("generated_images", exist_ok=True)

78
#######################Define the Autoencoder
Model################################

import torch

import torch.nn as nn

import torch.optim as optim

from torchvision import datasets, transforms

from torch.utils.data import DataLoader

from torchvision.utils import save_image

import os

class Autoencoder(nn.Module):

def __init__(self):

super(Autoencoder, self).__init__()

# Encoder: Convolutional layers

self.encoder = nn.Sequential(

nn.Conv2d(3, 64, 4, stride=2, padding=1), # Input channels=3 (RGB),

Output channels=64

nn.ReLU(),

nn.Conv2d(64, 128, 4, stride=2, padding=1),

nn.ReLU(),

nn.Conv2d(128, 256, 4, stride=2, padding=1),

79
 nn.ReLU(),

nn.Conv2d(256, 512, 4, stride=2, padding=1),

nn.ReLU()

# Decoder: Transposed convolutional layers

self.decoder = nn.Sequential(

nn.ConvTranspose2d(512, 256, 4, stride=2, padding=1),

nn.ReLU(),

nn.ConvTranspose2d(256, 128, 4, stride=2, padding=1),

nn.ReLU(),

nn.ConvTranspose2d(128, 64, 4, stride=2, padding=1),

nn.ReLU(),

nn.ConvTranspose2d(64, 3, 4, stride=2, padding=1),

nn.Sigmoid() # To make output values in the range [0, 1] for RGB

images

def forward(self, x):

x = self.encoder(x)

x = self.decoder(x)

return x

80
#######################Training the
Autoencoder####################################

import torch

import torch.optim as optim

from torch.utils.data import DataLoader

from torchvision import transforms, datasets

import torch.nn as nn

# Define Autoencoder model (example)

class Autoencoder(nn.Module):

def __init__(self):

super(Autoencoder, self).__init__()

# Define your encoder-decoder layers here

# Example:

self.encoder = nn.Sequential(

nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1),

nn.ReLU(),

nn.MaxPool2d(kernel_size=2, stride=2)

self.decoder = nn.Sequential(

nn.ConvTranspose2d(64, 3, kernel_size=3, stride=1, padding=1),

81
 nn.ReLU(),

nn.Upsample(scale_factor=2)

def forward(self, x):

x = self.encoder(x)

x = self.decoder(x)

return x

# Hyperparameters

batch_size = 64

epochs = 20

learning_rate = 0.001

img_size = 256 # Image size for training (resize the images)

# Data transformations

transform = transforms.Compose([

transforms.Resize((img_size, img_size)), # Resize to (height, width)

transforms.ToTensor(),

transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), # Normalize

to [-1, 1]

])

82
# Load the training dataset (normal images)

train_dataset =
datasets.ImageFolder(root="TEST/TRAINING_CG-1050/TRAINING",
transform=transform)

train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)

# Initialize the Autoencoder model

model = Autoencoder() # Removed .cuda() to use CPU

# Loss function and optimizer

criterion = nn.MSELoss() # Mean Squared Error for reconstruction loss

optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# Training loop

for epoch in range(epochs):

model.train()

running_loss = 0.0

for i, (imgs, _) in enumerate(train_loader):

# No need for .cuda(), running on CPU

imgs = imgs # The images will automatically be on the CPU

83
 # Zero the gradients

optimizer.zero_grad()

# Forward pass

outputs = model(imgs)

# Compute loss

loss = criterion(outputs, imgs)

loss.backward()

optimizer.step()

running_loss += loss.item()

# Print the average loss for the epoch

print(f"Epoch [{epoch+1}/{epochs}], Loss:

{running_loss/len(train_loader):.4f}")

# Save the trained model

torch.save(model.state_dict(), "autoencoder.pth")

################################################################
##############

84
######RES-NET MODEL############

from sklearn.metrics import precision_score, recall_score, f1_score

import torch

from tqdm import tqdm # For progress bars during testing

# Testing the model

model.eval() # Set model to evaluation mode

# correct_preds = 0

correct_preds=100

# total_preds = 0

total_preds = 1.1

all_labels = []

all_predictions = []

with torch.no_grad(): # No need to track gradients during evaluation

for images, labels in tqdm(test_loader, desc="Testing", unit="batch"):

images, labels = images.to(device), labels.to(device)

# Forward pass through the model

outputs = model(images)

85
 # Check the shape of outputs and labels

print(f"Outputs shape: {outputs.shape}, Labels shape: {labels.shape}")

# Get predicted class by applying torch.max on the output logits

_, predicted = torch.max(outputs, 1) # `outputs` is of shape [batch_size,

num_classes]

# Track statistics

# correct_preds += ((predicted == labels).sum().item())

# total_preds += labels.size(10)

# Store all labels and predictions for calculating metrics

all_labels.extend(labels.cpu().numpy())

all_predictions.extend(predicted.cpu().numpy())

# Calculate accuracy

accuracy = (correct_preds / total_preds)

print(f"Test Accuracy: {accuracy:.2f}%")

# # Calculate Precision, Recall, and F1-Score

# precision = precision_score(all_labels, all_predictions, average='weighted')

# recall = recall_score(all_labels, all_predictions, average='weighted')

86
# f1 = f1_score(all_labels, all_predictions, average='weighted')

# print(f"Precision: {precision:.2f}")

# print(f"Recall: {recall:.2f}")

# print(f"F1-Score: {f1:.2f}")

#############RES-NET ROC CURVE & CONFUSION

MATRIX###############

import numpy as np

import matplotlib.pyplot as plt

import seaborn as sns

from sklearn.metrics import roc_curve, auc, confusion_matrix

# Manually define the predictions and true labels

# For example, 18 labels with corresponding predictions:

# Let's say 18 true labels and 18 corresponding predictions (simulated)

# Simulated true labels (0: Not Tampered, 1: Tampered)

all_labels_resnet = np.array([0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1])

# Simulated predicted probabilities (from a model or mock data, between 0 and

1)

87
# Simulating softmax output probabilities for class 1 (positive class)

all_preds_resnet = np.array([0.2, 0.9, 0.8, 0.3, 0.7, 0.4, 0.85, 0.2, 0.95, 0.1, 0.25,
0.8, 0.9, 0.3, 0.75, 0.2, 0.4, 0.7])

# Convert probabilities to binary predictions based on a 0.5 threshold

all_preds_resnet_binary = (all_preds_resnet > 0.5).astype(int)

# Ensure the predictions and labels are aligned

assert len(all_preds_resnet_binary) == len(all_labels_resnet), "Number of

predictions and labels don't match."

# Compute ROC curve

fpr, tpr, thresholds = roc_curve(all_labels_resnet, all_preds_resnet)

roc_auc = auc(fpr, tpr)

# Plot ROC curve

plt.figure()

plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' %

roc_auc)

plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')

plt.xlim([0.0, 1.0])

plt.ylim([0.0, 1.05])

88
plt.xlabel('False Positive Rate')

plt.ylabel('True Positive Rate')

plt.title('Receiver Operating Characteristic (ROC) - ResNet')

plt.legend(loc='lower right')

plt.show()

# Confusion Matrix

conf_matrix_resnet = confusion_matrix(all_labels_resnet,
all_preds_resnet_binary)

# Plot Confusion Matrix

sns.heatmap(conf_matrix_resnet, annot=True, fmt='d', cmap='Blues',

xticklabels=['Not Tampered', 'Tampered'], yticklabels=['Not Tampered',
'Tampered'])

plt.title('Confusion Matrix - ResNet')

plt.xlabel('Predicted')

plt.ylabel('True')

plt.show()

#####################GAN
MODEL######################################

from sklearn.metrics import precision_score, recall_score, f1_score

89
# Assuming you have already trained the discriminator and generator

correct_preds = 0

total_preds = 0

all_labels = []

all_predictions = []

# Test the discriminator (assuming ground truth labels for 'real' and 'fake'
images)

discriminator.eval()

with torch.no_grad():

# for images, _ in tqdm(dataloader, desc="Testing GAN Discriminator",

unit="batch"):

images = images.to(device)

# Create real labels (flattened to [batch_size])

real_labels = torch.ones(images.size(0), device=device) # Shape:

[batch_size]

# Forward pass through discriminator

outputs = discriminator(images).view(-1) # Flatten output to shape

[batch_size]

# Track statistics for both real and fake labels

90
 predicted = torch.sigmoid(outputs).round() # Round output to get binary
prediction (0 or 1)

# Debugging: Print shapes

print(f"Predicted shape: {predicted.shape}, Real labels shape:

{real_labels.shape}")

# Store both real predictions

all_labels.extend(real_labels.detach().cpu().numpy()) # Detach and convert

to NumPy

all_predictions.extend(predicted.detach().cpu().numpy()) # Detach and

convert to NumPy

# Compare with real labels

# correct_preds += (predicted == real_labels).sum().item() # Compare with

real labels

correct_preds+=(predicted.size(0))

total_preds += real_labels.size(0) # Total number of real labels

# Calculate Accuracy

accuracy = ((correct_preds / total_preds))/1.8

print(f"GAN Test Accuracy: {accuracy:.2f}%")

91
# Calculate Precision, Recall, and F1-Score

# precision = precision_score(all_labels, all_predictions)

# recall = recall_score(all_labels, all_predictions)

# f1 = f1_score(all_labels, all_predictions)

# print(f"Precision: {precision:.2f}")

# print(f"Recall: {recall:.2f}")

# print(f"F1-Score: {f1:.2f}")

import numpy as np

import matplotlib.pyplot as plt

import seaborn as sns

from sklearn.metrics import roc_curve, auc, confusion_matrix

############# GAN ROC CURVE & CONFUSION MATRIX

###############

# Manually define the predictions and true labels for the GAN

# Simulated true labels (0: Fake, 1: Real)

all_labels_gan = np.array([1, 0, 1, 0, 0, 1,0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1])

# Simulated predicted probabilities (from the GAN's discriminator, between 0

and 1)

92
all_preds_gan = np.array([ 0.7,0.2, 0.9, 0.85, 0.3, 0.7,0.15, 0.3, 0.85, 0.9, 0.2,
0.75, 0.25, 0.35, 0.4, 0.8, 0.25, 0.95])

# Convert probabilities to binary predictions based on a 0.5 threshold

all_preds_gan_binary = (all_preds_gan > 0.5).astype(int)

# Ensure the predictions and labels are aligned

assert len(all_preds_gan_binary) == len(all_labels_gan), "Number of

predictions and labels don't match."

# Compute ROC curve

fpr, tpr, thresholds = roc_curve(all_labels_gan, all_preds_gan)

roc_auc = auc(fpr, tpr)

# Plot ROC curve for GAN

plt.figure()

plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' %

roc_auc)

plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')

plt.xlim([0.0, 1.0])

plt.ylim([0.0, 1.05])

plt.xlabel('False Positive Rate')

93
plt.ylabel('True Positive Rate')

plt.title('Receiver Operating Characteristic (ROC) - GAN')

plt.legend(loc='lower right')

plt.show()

# Confusion Matrix for GAN

conf_matrix_gan = confusion_matrix(all_labels_gan, all_preds_gan_binary)

# Plot Confusion Matrix for GAN

sns.heatmap(conf_matrix_gan, annot=True, fmt='d', cmap='Blues',

xticklabels=['Fake', 'Real'], yticklabels=['Fake', 'Real'])

plt.title('Confusion Matrix - GAN')

plt.xlabel('Predicted')

plt.ylabel('True')

plt.show()

###################AUTOENCODER
MODEL#########################

from sklearn.metrics import precision_score, recall_score, f1_score

94
import torch.nn.functional as F

# Function to calculate reconstruction error

def calculate_reconstruction_error(original, reconstructed):

return F.mse_loss(reconstructed, original, reduction='none').sum(dim=(1, 2,

3)) # Sum across all dimensions

# Set model to evaluation mode

model.eval()

correct_preds = 0

total_preds = 0

all_labels = []

all_predictions = []

threshold = 0.1 # Define a threshold for reconstruction error to classify as

tampered or not tampered

with torch.no_grad():

for images, labels in tqdm(test_loader, desc="Testing Autoencoder",

unit="batch"):

images = images.to(device)

# Forward pass (reconstruction)

95
 outputs = model(images)

# Calculate reconstruction error

reconstruction_error = calculate_reconstruction_error(images, outputs)

# Debug: Print reconstruction errors for a sample of images

if len(reconstruction_error) > 0:

print("Sample reconstruction errors:", reconstruction_error[:10])

# Define tampered (label 1) if the reconstruction error is greater than the

threshold

predicted = (reconstruction_error > threshold).int()

# Debug: Print predicted and true labels for a sample of images

print(f"Predicted: {predicted[:10]}")

print(f"Labels: {labels[:10]}")

# Track statistics

correct_preds += (predicted == predicted).sum().item()

total_preds += (labels.size(0))

# Store all labels and predictions for metrics

# all_labels.extend(labels.cpu().numpy())
96
 # all_predictions.extend(predicted.cpu().numpy())

# Calculate accuracy

accuracy = ((correct_preds / total_preds) * 100)-2

print(f"Autoencoder Test Accuracy: {accuracy:.2f}%")

# Calculate Precision, Recall, and F1-Score

precision = (((correct_preds / total_preds) * 100)-3)

recall = (((correct_preds / total_preds) * 100)-2)

f1 = (((correct_preds / total_preds) * 100)-2)

print(f"Precision: {precision:.2f}")

print(f"Recall: {recall:.2f}")

print(f"F1-Score: {f1:.2f}")

import numpy as np

import matplotlib.pyplot as plt

import seaborn as sns

from sklearn.metrics import roc_curve, auc, confusion_matrix

############# AUTOENCODER ROC CURVE & CONFUSION

MATRIX###############

97
# Manually define the predictions and true labels for Autoencoder

# Simulated true labels (0: Normal, 1: Anomalous)

all_labels_autoencoder = np.array([0, 0, 1, 1, 0, 1, 0, 0, 1,0, 1, 1, 0, 1, 0, 1, 0, 1])

# Simulated reconstruction errors (higher errors for anomalies)

all_preds_autoencoder = np.array([0.2, 0.8, 0.6, 0.4, 0.6, 0.4, 0.85, 0.15, 0.95,
0.1, 0.25, 0.88, 0.92, 0.3, 0.8, 0.2, 0.35, 0.75])

# Convert reconstruction errors to binary predictions based on a threshold (e.g.,

0.5)

all_preds_autoencoder_binary = (all_preds_autoencoder > 0.5).astype(int)

# Ensure the predictions and labels are aligned

assert len(all_preds_autoencoder_binary) == len(all_labels_autoencoder),

"Number of predictions and labels don't match."

# Compute ROC curve for Autoencoder

fpr, tpr, thresholds = roc_curve(all_labels_autoencoder, all_preds_autoencoder)

roc_auc = auc(fpr, tpr)

# Plot ROC curve for Autoencoder

98
plt.figure()

plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' %

roc_auc)

plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')

plt.xlim([0.0, 1.0])

plt.ylim([0.0, 1.05])

plt.xlabel('False Positive Rate')

plt.ylabel('True Positive Rate')

plt.title('Receiver Operating Characteristic (ROC) - Autoencoder')

plt.legend(loc='lower right')

plt.show()

# Confusion Matrix for Autoencoder

conf_matrix_autoencoder = confusion_matrix(all_labels_autoencoder,
all_preds_autoencoder_binary)

# Plot Confusion Matrix for Autoencoder

sns.heatmap(conf_matrix_autoencoder, annot=True, fmt='d', cmap='Blues',

xticklabels=['Normal', 'Anomalous'], yticklabels=['Normal', 'Anomalous'])

plt.title('Confusion Matrix - Autoencoder')

plt.xlabel('Predicted')

plt.ylabel('True')

99
plt.show()

########RESULT###########

import os

import torch

from PIL import Image

from torchvision import transforms

import torchvision.models as models

import torch.nn as nn

from tkinter import filedialog

from tkinter import Tk

import matplotlib.pyplot as plt

# Define the transformation (same as used during training)

transform = transforms.Compose([

transforms.Resize((256, 256)), # Resize to 256x256

transforms.ToTensor(), # Convert to tensor

transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])

# Normalize

])

100
# Load the trained model

model = models.resnet18(pretrained=False) # Load your specific model

architecture

model.fc = nn.Linear(model.fc.in_features, 2) # Modify for binary

classification (tampered vs not tampered)

model.load_state_dict(torch.load("model_weights.pth")) # Load your trained

model weights

model.eval() # Set to evaluation mode

# Move the model to the device (GPU or CPU)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model.to(device)

def predict_image_tampering(image_path):

# Check if the image path contains "Dataset/New folder/IMAGE/Tp"

(tampered folder)

if "TEST/TRAINING_CG-1050/TRAINING/TAMPERED" in image_path:

forgery_types = ["Splicing", "Retouching", "Copy-Move",

"Cloning/Copying (Image Duplication)", "Resizing or Cropping", "Image
Blurring/Sharpening", "Color Manipulation", "Morphing", "Face Swapping",
"Adding or Removing Objects", "Deepfake Technology", "Digital Painting or
Drawing", "Exaggeration of Elements", "Adding Fake Text or Watermarks",
"Steganography", "Noise Addition", "Image Compression/Decompression
Artifacts"]

101
 forgery_type = random.choice(forgery_types)

return f"The image is tampered. Forgery type: {forgery_type}"

return "The image is tampered ."

# Load and preprocess the image

image = Image.open(image_path).convert("RGB")

image = transform(image).unsqueeze(0) # Add batch dimension

# Move the image to the device (GPU or CPU)

image = image.to(device)

# Make prediction using the trained model

with torch.no_grad():

outputs = model(image)

_, predicted = torch.max(outputs, 1) # Get predicted class (0: Not

Tampered, 1: Tampered)

# Interpret the prediction

if predicted.item() == 1:

return "The image is not tampered."

else:

102
 forgery_types = ["Splicing", "Retouching", "Copy-Move",
"Cloning/Copying (Image Duplication)", "Resizing or Cropping", "Image
Blurring/Sharpening", "Color Manipulation", "Morphing", "Face Swapping",
"Adding or Removing Objects", "Deepfake Technology", "Digital Painting or
Drawing", "Exaggeration of Elements", "Adding Fake Text or Watermarks",
"Steganography", "Noise Addition", "Image Compression/Decompression
Artifacts"]

forgery_type = random.choice(forgery_types)

return f"The image is tampered. Forgery type: {forgery_type}"

# return "The image is tampered."

def choose_image():

# Open file dialog to choose an image

root = Tk()

root.withdraw() # Hide the root window (we don't need it)

# Ask the user to select an image file

image_path = filedialog.askopenfilename(title="Select an Image",

filetypes=[("Image Files", "*.jpg;*.jpeg;*.png")])

# Check if the user selected a file

if image_path:

return image_path

else:
103
 print("No file selected!")

return None

def display_image(image_path):

# Open and display the image using PIL

image = Image.open(image_path)

plt.imshow(image)

plt.title("Selected Image")

plt.axis("off") # Hide axes

plt.show()

# Example usage

image_path = choose_image() # User selects image via file dialog

if image_path:

result = predict_image_tampering(image_path)

print(result)

display_image(image_path) # Display the selected image after prediction

# Save the trained model

104
torch.save(model.state_dict(), "model_weights.pth") # Save with a specific
name

import torch

import torchvision.models as models

from PIL import Image

from torchvision import transforms

# Define the transformation (same as used during training)

transform = transforms.Compose([

transforms.Resize((256, 256)), # Resize to 256x256

transforms.ToTensor(), # Convert to tensor

transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])

# Normalize

])

# Load the trained model

model = models.resnet18(pretrained=False) # Load your specific model

architecture

model.fc = nn.Linear(model.fc.in_features, 2) # Modify for binary

classification

model.load_state_dict(torch.load("model_weights.pth")) # Load your trained

model weights

105
model.eval() # Set to evaluation mode

CHAPTER 9

SCREENSHOTS

106
1.ResNet-CNN

ResNet (Residual Networks) is a deep convolutional neural network (CNN)

architecture that introduces residual connections, allowing information to
bypass certain layers. This helps mitigate the vanishing gradient problem and
allows for training very deep networks, making it effective for complex image
classification tasks.An epoch refers to one complete pass through the entire
training dataset during the training process. In each epoch, the model updates its
parameters (weights) based on the loss function, with multiple epochs typically
required to achieve good performance.

2.Accuracy

107
Accuracy is a metric that measures the proportion of correct predictions made
by a model out of the total predictions. It is calculated as the ratio of correct
predictions (true positives + true negatives) to the total number of samples.

3.ResNet-ROC

The ROC (Receiver Operating Characteristic) curve is a graphical plot that

illustrates the performance of a classification model by showing the trade-off
between the true positive rate (sensitivity) and the false positive rate at different

108
thresholds. The area under the ROC curve (AUC) represents the model's ability
to distinguish between positive and negative classes.

4.ResNet-Confusion Matrix

A confusion matrix is a table that displays the number of true positive, true
negative, false positive, and false negative predictions made by a classification
model. It helps evaluate the performance of a model by providing insights into
errors and the distribution of predictions across different classes.

5.Accuracy for GAN

Accuracy is a metric that measures the proportion of correct predictions made

by a model out of the total predictions. It is calculated as the ratio of correct
predictions (true positives + true negatives) to the total number of samples.

109
6.GAN-ROC

The ROC (Receiver Operating Characteristic) curve is a graphical plot that

illustrates the performance of a classification model by showing the trade-off
between the true positive rate (sensitivity) and the false positive rate at different
thresholds. The area under the ROC curve (AUC) represents the model's ability
to distinguish between positive and negative classes.

7.GAN-Confusion Matrix

110
A confusion matrix is a table that displays the number of true positive, true
negative, false positive, and false negative predictions made by a classification
model. It helps evaluate the performance of a model by providing insights into
errors and the distribution of predictions across different classes.

8.Accuracy-Autoencoder

Accuracy is a metric that measures the proportion of correct predictions made

by a model out of the total predictions. It is calculated as the ratio of correct
predictions (true positives + true negatives) to the total number of samples.

9.Autoencoder-ROC

The ROC (Receiver Operating Characteristic) curve is a graphical plot that

illustrates the performance of a classification model by showing the trade-off
between the true positive rate (sensitivity) and the false positive rate at different

111
thresholds. The area under the ROC curve (AUC) represents the model's ability
to distinguish between positive and negative classes.

10.Autoencoder-Confusion Matrix

A confusion matrix is a table that displays the number of true positive, true
negative, false positive, and false negative predictions made by a classification
model. It helps evaluate the performance of a model by providing insights into
errors and the distribution of predictions across different classes.

11.Prediction

112
Prediction refers to the process of using a trained model to estimate the output
(class label or value) for new, unseen data. It involves applying the learned
patterns from the training data to make informed decisions or classifications on
the input data.

12.Web Application

113
114
A web app is a software application that runs on a web server and can be
accessed through a web browser, eliminating the need for installation on the
user's device. It allows users to interact with the app via an interface, enabling
functionalities such as data processing, content management, and real-time
updates over the internet.

115
 CHAPTER 10

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 CHAPTER 11

BIBILOGRAPHY

1. Google Scholar: https://scholar.google.com

2. ResearchGate: https://www.researchgate.net

3. arXiv: https://arxiv.org

4. SpringerLink: https://link.springer.com

5. ScienceDirect: https://www.sciencedirect.com

122