Deep Fake Detection Using Deep Learning

Abstract

The emerging Deep fake Detection System has been a major threat to digital media integrity since its introduction in 2017; early applications of the technology have been largely based on applications of face- swapping techniques using auto encoders and generative adversarial networks (GANs).Owing to the fast pace of development of these synthetic media generation techniques, these methods have outpaced more traditional detection methods, leading to an urgent need for more adaptive solutions. The system creates a solution to the problem by designing a new self-learning detection system that does not require pre-existing datasets. Compared to traditional methods, which have to be provided with large amounts of labelled data, our implementation uses incremental machine learning algorithms that are incrementally refined based on user feedback and confidence-based learning mechanisms. By leveraging transfer learning and online learning paradigms, the platform learns to adapt to emerging deep fake variations in real-time. Results of current implementation show promising results in detecting artifacts from eye blinking patterns, facial micro-expressions and audio-visual synchronization errors. Further work will be done to incorporate transformer-based architectures for better temporal analysis, add text deep fakes detection. The project will serve to build an open source, continually improving defence against synthetic media manipulation that will further increase the authenticity and security of digital media.