BMS INSTITUTE OF TECHNOLOGY AND MANAGEMENT

Autonomous Institute under VTU, Belagavi, Karnataka - 590 018

Yelahanka, Bengaluru, Karnataka - 560 119

Natural Langauge Processing

(BCS703A)
Mini Project Report
On

Fake News + Deepfake Cross-Validation System

BACHELOR OF ENGINEERING

in

COMPUTER SCIENCE AND ENGINEERING

by

Vansh Neggi 1BY22CS192

Samanth D 1BY22CS160
Shreya M 1BY22CS167

Under the Guidance of

Prof Gururaj P
Assistant Professor,
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING

Avalahalli, Yelahanka, Bengaluru, Karnataka -560119

August 2025
 Executive Summary
The proliferation of misinformation through digital media has become a critical
challenge in the contemporary information landscape. Traditional approaches to
combating fake news typically focus on either textual analysis or visual content
verification in isolation. This project proposes and develops a novel Multimodal
Cross-Validation System that integrates both fake news detection and deepfake
identification within a unified framework.
The system employs advanced Natural Language Processing (NLP) techniques,
Computer Vision algorithms, and Cross-Modal Consistency Verification to provide
comprehensive misinformation detection. Unlike existing single-modality solutions,
this approach validates textual claims against accompanying visual content, metadata
analysis, and contextual information to deliver explainable results with confidence
scoring.
Key Contributions:
 First integrated cross-modal verification system combining text and visual content
analysis
 Novel cross-consistency validation framework for multimodal misinformation
detection
 Explainable AI implementation providing detailed reasoning for classification
decisions
 Comprehensive metadata and contextual analysis integration

1. Introduction
1.1 Background
The digital age has witnessed an unprecedented surge in information dissemination
through social media platforms, online news portals, and messaging applications.
While this democratization of information sharing has numerous benefits, it has
simultaneously created fertile ground for the spread of misinformation. The challenge
is compounded by the emergence of sophisticated deepfake technologies that can
create convincing but fabricated visual content.

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 1.2 Problem Statement
Current misinformation detection systems suffer from several limitations:
 Fragmented Approach: Existing solutions typically address either textual fake news
or visual deepfakes separately
 Limited Context Analysis: Most systems fail to verify consistency between textual
claims and accompanying media
 Lack of Explainability: Many detection systems provide binary classifications
without explaining the reasoning
 Metadata Oversight: Insufficient utilization of temporal, geographical, and technical
metadata for verification
1.3 Objectives
The primary objectives of this project are:
1. Develop an Integrated System: Create a unified platform that simultaneously
analyzes textual content and visual media
2. Implement Cross-Modal Validation: Establish consistency checks between different
content modalities
3. Provide Explainable Results: Generate detailed explanations for classification
decisions
4. Ensure Scalability: Design a system capable of handling real-world deployment
scenarios
5. Achieve High Accuracy: Deliver superior performance compared to existing single-
modality approaches

2. Literature Review
2.1 Textual Fake News Detection
Traditional fake news detection has primarily relied on Natural Language Processing
techniques. Early approaches utilized linguistic features such as word frequency,
sentiment analysis, and readability metrics. Recent advancements have incorporated
transformer-based models like BERT and RoBERTa, achieving significant
improvements in classification accuracy.
Key Limitations:

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  Inability to verify claims against visual evidence
 Vulnerability to sophisticated writing styles that mimic legitimate journalism
 Limited effectiveness against multimedia misinformation campaigns
2.2 Deepfake and Media Manipulation Detection
Visual content verification has evolved from simple image forensics to sophisticated
deep learning approaches. Technologies like XceptionNet, FaceForensics++, and
specialized CNN architectures have demonstrated effectiveness in identifying
manipulated visual content.
Key Limitations:
 Focus solely on technical manipulation detection
 Inability to verify content authenticity in context
 Limited integration with textual claim verification
2.3 Research Gap
The literature reveals a significant gap in multimodal misinformation detection
systems that can:
 Simultaneously process and validate textual and visual content
 Perform cross-modal consistency verification
 Provide comprehensive explainability for classification decisions
 Integrate metadata and contextual analysis

3. Methodology
3.1 System Architecture
The proposed system comprises six interconnected modules:
3.1.1 Textual Analysis Module
 Preprocessing Pipeline: Text cleaning, tokenization, and normalization
 Feature Extraction: BERT/RoBERTa embeddings for semantic representation
 Classification: Fine-tuned transformer models for credibility assessment
 Fact-Checking Integration: Cross-reference with verified databases (PolitiFact,
Snopes)
3.1.2 Visual Content Analysis Module

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  Video Processing: Key frame extraction and temporal analysis
 Deepfake Detection: XceptionNet-based manipulation identification
 Image Verification: Reverse image search and authenticity validation
 Technical Analysis: EXIF data extraction and forensic examination
3.1.3 Cross-Modal Consistency Verification
 Semantic Alignment: CLIP model implementation for text-image similarity
assessment
 Contextual Matching: Geographic and temporal consistency validation
 Content Verification: Object and entity recognition across modalities
 Narrative Consistency: Storyline coherence analysis
3.1.4 Metadata and Context Analysis
 Temporal Verification: Timestamp analysis and chronological consistency
 Geographic Validation: Location data extraction and verification
 Source Analysis: Publisher credibility and distribution pattern analysis
 Social Context: Engagement pattern and propagation analysis
3.1.5 Explainability Engine
 Mismatch Identification: Detailed inconsistency reporting
 Confidence Scoring: Probabilistic assessment with uncertainty quantification
 Evidence Highlighting: Visual and textual evidence presentation
 Reasoning Chain: Step-by-step decision process documentation
3.1.6 Integration and Decision Layer
 Feature Fusion: Multi-modal feature combination strategies
 Ensemble Classification: Random Forest/XGBoost for final decision making
 Output Generation: Structured result presentation with explanations
 API Interface: Standardized endpoints for system integration
3.2 Implementation Framework
3.2.1 Data Collection and Preparation
 Textual Datasets: LIAR dataset, FakeNewsNet, custom news article collections
 Visual Datasets: FaceForensics++, DFDC, curated image-text pairs

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 Metadata Integration: EXIF data, social media timestamps, geographic tags
 Ground Truth Establishment: Expert annotation and verification processes
3.2.2 Model Training and Optimization
 Transfer Learning: Pre-trained model fine-tuning for domain adaptation
 Multi-task Learning: Joint optimization across detection objectives
 Cross-validation: Robust evaluation with multiple data splits
 Hyperparameter Optimization: Grid search and Bayesian optimization
3.2.3 System Integration
 Modular Design: Loosely coupled components for maintainability
 API Development: RESTful services for component communication
 Database Integration: Efficient storage and retrieval mechanisms
 User Interface: Intuitive dashboard for system interaction

4. Expected Results and Impact

4.1 Performance Metrics
4.1.1 Classification Accuracy
 Target Accuracy: >92% for integrated multimodal classification
 Precision/Recall: Balanced F1-score optimization
 False Positive Rate: <5% to minimize legitimate content flagging
 Cross-Modal Consistency: >88% accuracy in detecting content mismatches
4.1.2 Explainability Metrics
 Explanation Quality: Human evaluation scores for reasoning clarity
 Feature Attribution: SHAP values for model interpretability
 Confidence Calibration: Reliability of uncertainty estimates
 User Comprehension: Interface usability assessments
4.2 System Capabilities
4.2.1 Real-World Application Scenarios
 News Verification: Automated fact-checking for journalism
 Social Media Monitoring: Platform-integrated misinformation detection

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  Educational Tools: Media literacy training applications
 Research Applications: Academic misinformation studies
4.2.2 Scalability Demonstrations
 Processing Speed: Real-time analysis capability
 Batch Processing: Large-scale content verification
 API Performance: High-throughput service delivery
 Resource Efficiency: Optimized computational resource utilization
4.3 Innovation Impact
4.3.1 Technical Contributions
 Novel Architecture: First comprehensive multimodal misinformation detection
system
 Methodological Advances: Cross-modal consistency verification techniques
 Explainability Enhancement: Advanced reasoning and explanation generation
 Integration Innovation: Seamless multi-component system design
4.3.2 Societal Benefits
 Misinformation Reduction: Enhanced detection and prevention capabilities
 Media Literacy: Educational tool for critical information consumption
 Platform Security: Improved content moderation for social media
 Democratic Protection: Safeguarding against election misinformation

5. Implementation Timeline
Phase 1: Foundation Development (Weeks 1-4)
 Dataset collection and preprocessing
 Individual module development and testing
 Base model training and validation
 Initial integration framework setup
Phase 2: Cross-Modal Integration (Weeks 5-8)
 CLIP model implementation and fine-tuning
 Cross-consistency verification algorithm development

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  Metadata analysis module integration
 Preliminary system testing
Phase 3: Explainability and Optimization (Weeks 9-12)
 Explainability engine development
 Performance optimization and tuning
 User interface design and implementation
 Comprehensive system testing
Phase 4: Validation and Deployment (Weeks 13-16)
 Real-world dataset testing
 Performance benchmarking
 API development and documentation
 Final system deployment and evaluation

6. Technical Specifications
6.1 Hardware Requirements
 GPU: NVIDIA RTX 4090 or equivalent (24GB VRAM minimum)
 CPU: Multi-core processor with 32GB+ RAM
 Storage: SSD with 500GB+ available space
 Network: High-speed internet for API integrations
6.2 Software Framework
 Programming Language: Python 3.9+
 Deep Learning: PyTorch, Transformers (HuggingFace)
 Computer Vision: OpenCV, Pillow, CLIP
 NLP: spaCy, NLTK, sentence-transformers
 Web Framework: FastAPI, Streamlit
 Database: PostgreSQL, MongoDB
6.3 Model Specifications
 Text Models: BERT-base, RoBERTa-large
 Vision Models: XceptionNet, ResNet-50

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  Multimodal: CLIP ViT-B/32
 Ensemble: Random Forest, XGBoost

7. Risk Analysis and Mitigation

7.1 Technical Risks
 Model Performance: Regular retraining and validation protocols
 Scalability Issues: Cloud infrastructure and load balancing
 Integration Complexity: Modular design and comprehensive testing
 Data Quality: Robust preprocessing and validation pipelines
7.2 Ethical Considerations
 Privacy Protection: Data anonymization and secure processing
 Bias Mitigation: Diverse training data and fairness evaluations
 False Positives: Conservative thresholding and human oversight
 Transparency: Open-source components and documentation

8. Conclusion
The Multimodal Cross-Validation System for Fake News and Deepfake Detection
represents a significant advancement in misinformation detection technology. By
integrating textual analysis, visual content verification, and cross-modal consistency
checking within a unified framework, this system addresses critical gaps in current
misinformation detection approaches.
The project's novel contributions include the first comprehensive multimodal
verification system, advanced explainability features, and robust metadata
integration. The expected outcomes demonstrate potential for substantial impact in
combating misinformation across various digital platforms and applications.
The successful implementation of this system will provide a foundation for future
research in multimodal misinformation detection while offering immediate practical
benefits for news verification, social media content moderation, and educational
applications.

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 References
1. Wang, W. Y. (2017). "Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake
News Detection. Proceedings of the 55th Annual Meeting of the Association for
Computational Linguistics.
2. Rossler, A., et al. (2019). FaceForensics++: Learning to Detect Manipulated Facial
Images. Proceedings of the IEEE International Conference on Computer Vision.
3. Radford, A., et al. (2021). Learning Transferable Visual Models From Natural
Language Supervision. International Conference on Machine Learning.
4. Zellers, R., et al. (2019). Defending Against Neural Fake News. Advances in Neural
Information Processing Systems.
5. Li, Y., et al. (2020). In Ictu Oculi: Exposing AI Generated Fake Face Videos by
Detecting Eye Blinking. IEEE International Workshop on Information Forensics and
Security.

Appendices
 Appendix A: Detailed System Architecture Diagrams
 Appendix B: Dataset Specifications and Statistics
 Appendix C: Model Performance Benchmarks
 Appendix D: API Documentation and Usage Examples
 Appendix E: User Interface Mockups and Design Specifications

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