Detecting Deepfakes: MIT CSAIL Model Identifies Manipulations Using Local Artifacts

When celebrity porn and other deepfake videos went viral several years back they caught the world largely unprepared -- few could believe just how convincingly AI had generated the fake images. We have since seen numerous breakthroughs in image synthesis algorithms and face-synthesizing and swapping technologies enabled by generative adversarial networks (GANs), making deepfakes even more believable. Governmental and other bodies meanwhile have been scrambling to catch up -- looking for ways to counter the malicious spread of deepfakes which are now so realistic they can be difficult if not impossible for the human eye to detect. A team of researchers from MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) have proposed a new model that is designed to spot deepfakes by looking at subtle visual artifacts such as textures in hair, backgrounds, and faces, and visualizing image regions where it has detected manipulations. The team noted that as SOTA image synthesis techniques continue advancing under novel synthesis algorithms, it is critical that fake image detection methods keep step to enable efficient and robust identification of deepfakes created using such new methods. In this regard it is important to understand which artifacts the fake image detectors will examine if they are to remain effective in the face of continually evolving synthesis algorithms.