Deepfake Image Authentication with Deep Learning Technologies

Abstract

As synthetic media has grown, deepfake images have become a big danger to the truthfulness of information and trust in digital media. These pictures were made by advanced generative models like GANs (Generative Adversarial Networks). They often include small visual differences that the human eye can't see. The goal of this project is to create a strong deep learning-based system that can find and sort deepfake photos with great accuracy. The model is trained on benchmark datasets like Face Forensics++ and Celeb-DF using Convolutional Neural Networks (CNNs) and transfer learning techniques. The suggested system focusses on learning features that can tell the difference between actual and fake photographs. It does this by looking for patterns like pixel-level aberrations, irregular illumination, and face asymmetry. The experimental results suggest that the model can accurately identify deepfake content, which is important for digital forensics, social media moderation, and information security.