Deepfake Forensics, an AI-synthesized Detection with Deep Convolutional Generative Adversarial Networks
Dafeng Gong
Abstract
Recently, artificial intelligence, deep learning and Generative Adversarial Networks (GANs) adaptabilities for deepfake detection and forensics have become an emerging field of research interest. GANs have been widely studied since it was first proposed, and many applications have been produced to generate contents such as videos and images. The application of these new technologies in many fields makes it more and more difficult to distinguish between true and fake content. This study analyzes more than hundred published papers related to the application of GANs technology in various fields to generate digital multimedia data and expounds the technologies that can be used to identify deepfakes, the benefits and threats of deepfake technology, and how to crack down deepfakes. The findings indicate that although deepfakes pose a major threat to our society, politics and commerce, a variety of means are listed to limit the production of unethical and illegal deepfakes. Finally, the study also puts forward its limitations and possible future research directions and recommendations.