Deep Fake Detection Using Deep Learning : For Image And Video And Audio

Abstract

This project proposes a comprehensive Deepfake Detection system using advanced Deep Learning methodologies to automatically analyze and identify manipulated images and videos. Deepfakes, generated using sophisticated generative techniques such as Generative Adversarial Networks (GANs), pose a serious threat to digital trust by enabling the spread of misinformation, identity theft, and reputational damage. To combat this, the system employs Convolutional Neural Networks (CNNs) for spatial image analysis and a hybrid CNN + LSTM architecture to capture subtle temporal inconsistencies across video frames. Unlike traditional handcrafted feature-based methods, this deep learning approach directly learns discriminative patterns from large benchmark datasets such as FaceForensics++ and DFDC. The platform not only preprocesses media inputs and trains high-performance models but also classifies content as Real or Fake with high accuracy, offering a robust defense mechanism against the rising tide of misinformation and digital manipulation.