Deepfake Unmasking and Detection

This paper introduces the Deepfakes, AI-generated synthetic media that manipulate images and videos, have emerged as a serious challenge in digital security, privacy, and misinformation. This project aims to develop a robust deepfake detection system using cutting-edge machine learning techniques. By employing convolutional neural networks (CNNs) and leveraging pre trained models, the system identifies key indicators of tampering such as facial inconsistencies, unnatural eye movements, and pixel-level artifacts. The project incorporates diverse datasets like DeepFake Detection Challenge (DFDC) and FaceForensics++ to train and evaluate the system’s performance. Additionally, the solution integrates forensic techniques such as frame by-frame analysis and audio-visual synchronization checks for enhanced accuracy. Experimental results demonstrate high detection precision and recall rates, making the system effective for realworld applications, including social media monitoring and media verification platforms. This project contributes to combating the growing issue of deepfakes by providing an effective and scalable detection mechanism.