Analyzing Deepfake Image Detection with Vision Transformers and Explainable AI Techniques

1. Introduction 1.1 Background and Motivation 1.2 Problem Statement 1.3 Objectives of the Study 1.4 Structure of the Thesis 2. Literature Review 2.1 Deepfake Technology Overview 2.2 Image Detection Techniques 2.3 Vision Transformers in Computer Vision 2.4 Explainable AI in Deep Learning 3. Deepfake Image Detection Challenges 3.1 Technical Difficulties 3.2 Ethical Considerations 3.3 Social Impact 4. Vision Transformers 4.1 Architecture and Functionality 4.2 Applications in Deepfake Detection 4.3 Comparative Analysis 5. Explainable AI Techniques 5.1 Importance of Explainability 5.2 Methods for Explainable AI 5.3 Integration with Vision Transformers 6. Experimental Methodology 6.1 Dataset Description 6.2 Model Training and Optimization 6.3 Evaluation Metrics 7. Results and Discussion 7.1 Detection Performance 7.2 Interpretability of Results 7.3 Limitations and Improvements 8. Conclusion and Future Work 8.1 Summary of Findings 8.2 Implications for Practice 8.3 Suggestions for Future Research