Explainable Deepfake Detection using Deep Learning

Deepfakes, which facilitate the production of incredibly realistic yet synthetic audiovisual content, have emerged as a significant threat to the integrity of digital media and public trust. While many deep learning-based detection techniques demonstrate high accuracy, they often operate as "black boxes," providing minimal insight into their decision-making processes. This inherent lack of transparency poses challenges for adoption, verifiability, and legal accountability. This paper presents a comprehensive framework for explainable deepfake detection in both images and videos, designed to bridge the gap between detection efficacy and interpretability. Our approach integrates a refined ResNet18 classification model with effective face extraction using Multi-task Cascaded Convolutional Networks (MTCNN). For video analysis, we introduce a "golden frame" filtering method to select high-quality, informative frames, thereby enhancing training reliability and inference efficiency. Crucially, the system incorporates Gradient-weighted Class Activation Mapping (Grad-CAM) to generate visual heatmaps. These heatmaps offer human-interpretable explanations by highlighting the specific facial regions and artifacts that contribute most to a given prediction (real or fake). The entire pipeline, including the explainability module, is implemented in a web-based application to facilitate real-time deepfake identification and interpretation. Experimental results on benchmark datasets such as Face Forensics++ and the DFDC Preview Dataset demonstrate the system's high classification accuracy and the practical utility of the generated visual explanations in understanding the model's predictions. This work contributes a robust and scalable solution to the deepfake problem, emphasizing the critical need for explainability in AI-driven detection systems.