# Explainable AI for DeepFake Detection
## 6 Conclusions  
The rise of technology in the multimedia sector has brought about various malicious activities, including the exploitation of fabricated images and videos in areas such as entertainment and politics. This advancement has blurred the line between manipulated and authentic visuals, prompting extensive research in this field. The term 'deepfake' is now widely recognized for its ability to create highly realistic imagery. To deal with the latest manipulation methods in visual media, an explainable deepfake detection approach is critical. This research focuses on developing a deepfake detection method that delivers anexplainable model. The proposed research study involves the detection and interpretation of deepfake detection models using the network dissection algorithm. This is implemented in two stages. (1) To detect forged images, three cutting-edge CNNs (VGG-16, Inception V3, and ResNet-50) are used, and a comparative analysis of these models is performed. (2) A network dissection algorithm is employed to interpret and understand the internal parts of the models for the explainable model. The proposed network dissection algorithm has the ability to understand the internal representations of these models and thereby interpret the different facial concepts learned by them in classifying the images as real or fake. It is compulsory to note that the technology used to generate manipulated images is also utilized to detect them, demonstrating that scientific and technical breakthroughs are both a benefit and a curse. The experiments demonstrated that the CNN models employed in this study performed well, with F1-scores ranging from 0.8 to 0.9. The explainability part of the proposed study is based on a network dissection algorithm, which involves generating interpretability reports for the three deepfake detection models that have been trained on a well-known deepfake dataset. This also offers a comprehensive discussion on how model interpretability can be leveraged to gain a deeper understanding of the model mechanisms and the representation of information or features learned by these models. Although there are several deepfake detection tools and models available to detect manipulated videos or images, existing research lacks explanations or reasoning behind the classification of images when using the network dissection algorithm. This study aims to shed light on how these models distinguish between real and altered images by analyzing their internal neural representations. Additionally, while this study focuses on CNN architectures, it is important to note that the network dissection algorithm is specifically a strong interpretability tool for CNNs, as it is inherently designed to map internal CNN components to concepts comprehensible to humans. However, it is not directly applicable to architectures such as vision transformers (ViTs) or pure transformer models, which operate on fundamentally different principles. Nevertheless, the general principle of associating model components with concepts comprehensible to humans could inspire the development of similar techniques adapted for non-CNN architectures. As a future direction, adapting and extending the core ideas of the network dissection algorithm to these advanced architectures could provide deeper insights into their decision-making processes and enhance interpretability in tasks such as deepfake detection. This would bridge the gap in interpretability techniques across different model families, ensuring that the rapid advancements in model architectures are matched by equally sophisticated explainability methods. In conclusion, this study underscores the importance of interpretability in deep neural networks, providing insights into their hierarchical structures. By addressing the critical challenge of explainability in deepfake detection, this work not only improves the reliability of these systems but also lays the foundation for extending interpretability methods to more advanced architectures in future research.