Adversial Learning Through GAN and Feature Extraction Through CNN for Deep Fake Detection

Deepfake technology, using deep learning, creates highly realistic yet artificial media, creating challenges for security and privacy. Convolutional Neural Networks (CNNs) play a crucial role in detecting deep fakes by identifying minor inconsistencies in visual and auditory data, such as facial movement and audio-visual synchronization. This paper reviews CNN applications in deep fake detection, evaluating various architectures and their performance using standard parameters like accuracy and precision. It also addresses the need for large, diverse datasets and the evolving nature of deepfake techniques. The role played by machine learning and especially CNNs in this domain enhances the ability to detect and also protects digital trust and contributes to efforts aimed at detecting misinformation more broadly.