Interpretable and Trustworthy Deepfake Detection Framework: Leveraging Transfer-Learned CNNs with Grad-CAM and SHAP for Robust Media Forensics

The rapid evolution of Deep-fake technologies has enabled AI based techniques like Generative Adversarial Networks (GANs) to create incredibly realistic yet totally fabricated video and image content. These developments are very exciting; however, they have serious implications to many areas including; financial fraud, misinformation, Identity Theft and Erosion of Public Trust. A significant weakness of most existing detection mechanisms is the lack of transparency- Many operate as "Black Boxes" which will identify fake media but provide no explanation for why this was done; Therefore, Most are untrustworthy. The purpose of this research paper is to develop an advanced Deepfake Detection Framework that is capable of identifying manipulated media at a very high confidence level; In addition, Provide the user with a clear and understandable justification for every prediction. The framework uses transfer learning from pre-trained CNN models: Xception and ResNet50. It is trained on diverse, publicly available datasets and follows a structured preprocessing pipeline consisting of face detection, alignment, resizing, and data augmentation in order to improve real-world robustness. This is demonstrated by embedding Explainable AI (XAI) techniques, Grad-CAM and SHAP, that highlight the particular facial regions responsible for the model's prediction. For instance, the heatmap could convey that the eye or mouth region looks unnatural, which might hint that this is where the system bases its decision on whether something is fake. Combining compelling classification with both visual and numerical explanations will help the system to build users' confidence in its potential applications to real-world digital forensics, content moderation, and media verification tasks.