UNIVERSITY OF BUEA

Faculty of Science

Department of Computer Science

BACHELOR OF SCIENCE IN COMPUTER SCIENCE

Project Report
IMAGE PROCESSING USING PYTHON TO IDENTIFY OBJECTS IN IMAGES
( IMAGE MODIFICATION DETECTION AND IMAGE SIMILARITY)

RAMSEY IBE BENDE NDIVE

SC20C121

Supervisor
DR. MAGDALIENE NYAMSI

May 2023
 DECLARATION

This report has been written by me and has not received any previous academic credit at this or
any other institution.

RAMSEY IBE BENDE NDIVE

SC20C121
Department Of Computer Science
Faculty Of Science, University Of Buea

1
 CERTIFICATION
This is to certify that this report entitled “IMAGE PROCESSING USING PYTHON TO
IDENTIFY OBJECTS IN IMAGES ( IMAGE MODIFICATION AND IMAGE SIMILARITY
DETECTION )” is the original work of RAMSEY IBE BENDE NDIVE with Registration
Number SC20C121, student at the Department of Computer Science at the University of Buea.
All borrowed ideas and materials have been duly acknowledged by means of references and
citations. The report was supervised in accordance with the procedures laid down by the
University of Buea. It has been read and approved by:

DR. MAGDALIENE NYAMSI May 2023

(supervisor)

Dr. Denis L. Nkweteyim May 2023

Head of Department of Computer Science

2
 ACKNOWLEDGEMENT

I would like to thank my supervisor, DR. MAGDALIENE NYAMSI for her patience, discipline,
and guidance through this project. Without her help, this project would not have been possible.

3
 Abstract

The increasing prevalence of image manipulation techniques and the ease of modifying digital
images has led to a rise in the production of fake or modified images. This is an important issue
in various applications, including forensics analysis, image based attendance systems, where the
authenticity and accuracy of images are critical. Detecting image modification then solves this
problem.
This project will develop a system for image modification and image similarity detection.
The system will use a combination of image processing techniques and machine learning
algorithms. This system will be able to detect modified images and images that are similar to a
given image.
The system will be developed using the Python programming language and the OpenCV
library. The system will be tested on a variety of image datasets. The system will be evaluated
based on its ability to detect modified images and similar images.
The results of this project will be a system that can be used to detect modified images and similar
images. The system will be useful for a variety of applications, such as image forensics analysis,
image based attendance.

4
 INTRODUCTION
The purpose of this image modification and similarity detection application is to detect changes
made to an image. This can be useful in a variety of contexts, ranging from image based
attendance systems, image similarity. The application compares multiple versions of an image to
identify any differences or modifications made to the image. These modifications can include
editing or any other changes made to the image. The detection of these modifications can help to
identify potential fraud or manipulation of the images, or for comparison between images.

PROJECT BACKGROUND
Image modification and image similarity detection are two important tasks in computer vision.
Image modification detection is the task of detecting images or image parts that have been
tampered or manipulated (sometimes also referred to as doctored). This typically encompasses
image splicing, copy-move, or image inpainting[1].
Image Manipulation Detection | Papers With Code
Image similarity detection A fundamental computer vision task to determine whether a part of an
image has been copied from another image[2].
https://paperswithcode.com/task/image-similarity-detection
These tasks are used in a variety of applications, such as image editing, image search, and image
forensics. Image similarity detection can be used to find similar images, to find duplicate images,
or to identify images that have been tampered with.

Why I Thought the Project Was Needed

I thought this project was needed because there is a growing demand for systems that can detect
modified and similar images. The ability to detect modified images is useful for a variety of
applications, such as image forensics analysis. The ability to detect similar images is useful for a
variety of applications, such as finding similar images, finding duplicate images, and identifying
images that have been tampered with.

My Interest in the Project

I am interested in this project because I am passionate about computer vision. I believe that
image modification and image similarity detection are two important tasks in computer vision,
and i am excited to develop a system that can perform these tasks.

5
Technical or Social Background of the Project
The technical background of the project is in image processing and machine learning. Image
processing is the process of extracting information from images, and machine learning is the
process of learning from data. The system will use a combination of image processing techniques
and machine learning algorithms to detect modified images and similar images.
The social background of the project is in the field of computer vision. Computer vision is the
field of computer science that deals with the extraction of information from images and videos.
Computer vision is used in a variety of applications, such as image search, image forensics, and
image editing.

PROJECT AIM
The aim of this project is to develop a system that can detect images that have been modified and
images that are similar. The system will use a combination of image processing techniques and
machine learning algorithms. The system will be able to detect images that have been modified
in different ways such as by adjusting the brightness and contrast and applying filters. The
system will also be able to detect images that are similar to a given image.

PROJECT BOUNDARIES
I was not able to develop a system that was as easy to use as I would have liked. The system was
easy to use for someone who is familiar with programming, but it would be more user-friendly if
it had a graphical user interface. I was also not able to develop a system that was able to detect
modified and similar images in a reasonable amount of time on computers with low processing
power.

6
REPORT STRUCTURE
The rest of the report is given as follows. Chapter 1 talks about the Analysis and Design stage of
the research, how it was carried out, its functional and non-functional dependencies. Chapter 2
Shows code fragments on how key features were implemented and also shows the results
obtained from the implementation. It also talks about what happened and why it happened,
possible sources of errors with the application. Chapter 3 sums up the project and its implications
and suggestions for future research. Chapter 4 includes all the evidence that was cited in the
main body of the report using the citation and referencing style. Chapter 7 shows details for
setting up the application.

7
 CHAPTER 1
ANALYSIS AND DESIGN

PROBLEM STATEMENT
The widespread availability of digital image editing tools has made it easy to modify images in a
variety of ways, including adding or removing objects. This has led to an increase in the number
of images that have been manipulated, either intentionally or unintentionally. Image modification
can have negative consequences including:
Manipulated images can be used to spread misinformation, such as creating fake news
articles. Modified images can be used to infringe on copyrights, such as by creating unauthorized
copies of copyrighted works.
Image similarity detection is the task of finding images that are similar to a given image. This
can be useful for a variety of applications such as to find duplicate images and similar images.

RESEARCH QUESTIONS
Before and while carrying out this research, certain questions were raised such as;
1. What are the different types of image modifications that can be detected?
2. What are the different methods that can be used to detect image modifications?
3. What are the factors that affect the accuracy of image modification detection methods?
4. How can image modification detection methods be improved?
5. What are the advantages and disadvantages of the different methods for detecting image
modifications?
6. What are the different metrics that can be used to measure the similarity between images?
7. What are the different methods that can be used to find similar images?
8. What are the advantages and disadvantages of the different methods for finding similar
images?
9. How can image similarity detection be used to find duplicate images?
10. How can image similarity be used to find similar images?
11. How can image modification detection be used to ensure the integrity of evidence?
12. How can image modification detection be used to detect fake news and misinformation?

8
The paper tries to answer the questions and shed more light on areas for further work and
exploration.

RESEARCH METHOD
A literature review of previous research development in this topic area was conducted.
Image modification detection is the task of detecting images or image parts that have been
tampered or manipulated [1]. There are a number of different methods that can be used to detect
modification. Some of the most common methods include.
Statistical methods: Statistical methods use statistical properties of images to detect
changes. For example, statistical methods can be used to detect changes in the brightness,
contrast, or color of an image [3].
Feature-based methods: Feature-based methods identify features that are unique to an
image and then compare those features to features of other images. If the features of an image are
significantly different from the features of other images, then the image is likely to have been
modified [3] [4].
Deep learning methods: Deep learning methods use artificial neural networks to detect
image similarity. Deep learning methods can learn to identify patterns in images that are
indicative of similarity [5] [6].
Image modification detection and image similarity detection are important tools that can be used
for a variety of purposes. The research on these topics is constantly evolving, and new methods
are being developed all the time. As these methods become more accurate and reliable, they will
become increasingly important for a variety of applications.

FUNCTIONAL AND NON - FUNCTIONAL REQUIREMENTS

Functional Requirements
1. The application should be able to detect whether an image has been modified.
1.1. The application can detect whether an image has been modified by comparing the
image to another unmodified image. If the image is different from the unmodified
image, then it has been modified.

9
 1.2. The application can also detect whether an image has been modified by using a
variety of image processing techniques, such as:
1.2.1. Image differencing: This technique compares the pixel values of two
images to identify differences.
1.2.2. Feature extraction: This technique extracts features from an image, such
as edges, shapes, and colors. The features can then be used to identify the
image or to compare to another image.
1.2.3. Machine learning: This technique can be used to train a model to identify
images that have been modified.

2. The application should be able to identify where the modifications have been made on the
image.
2.1. The application can identify the specific part of the image where the
modifications have been done by adding the bounding box around the area.
3. The application should be able to calculate the similarity between two images
3.1. The project can calculate the similarity between two images by comparing the
pixel values of the images. The similarity between two images is a measure of
how similar the images are.
3.2. The project can also calculate the similarity between two images by using a
variety of image processing techniques, such as:
3.2.1. Image differencing: This technique compares the pixel values of two
images to identify differences.
3.2.2. Feature extraction: This technique extracts features from an image, such
as edges, shapes, and colors. The features can then be used to identify the
image or to compare it to other images.
3.2.3. Machine learning: This technique can be used to train a model to identify
images to identify images that are similar.

Non - Functional Requirements

10
These requirements are seen as attributes to the functional requirements. Functional requirements
are implemented with them in mind and someplace more emphasis on one or more of them than
others.
1. The application should be efficient and should not take too long to detect or calculate
image similarity. This is important because the user should not have to wait a long time
for the application to finish detecting or calculate image similarity.
2. The application should be robust and should be able to handle images of different sizes
and formats. This is important because the user should be able to use the application to
detect or calculate similarity for any image, regardless of its size or format.
3. The application should be accurate and should be able to correctly detect and identify
images that have been modified. This is important because the application will be used to
make important decisions, such as whether to accept an image as evidence or not.

11
12