Why Do Facial Deepfake Detectors Fail?
It addresses the problem of unreliable deepfake detectors for security and authentication, but is incremental as it reviews existing challenges without proposing a new solution.
This study identifies challenges in facial deepfake detection, including issues with artifact pre-processing and the failure to account for new, unseen deepfake generators, highlighting the unreliability of current detectors.
Recent rapid advancements in deepfake technology have allowed the creation of highly realistic fake media, such as video, image, and audio. These materials pose significant challenges to human authentication, such as impersonation, misinformation, or even a threat to national security. To keep pace with these rapid advancements, several deepfake detection algorithms have been proposed, leading to an ongoing arms race between deepfake creators and deepfake detectors. Nevertheless, these detectors are often unreliable and frequently fail to detect deepfakes. This study highlights the challenges they face in detecting deepfakes, including (1) the pre-processing pipeline of artifacts and (2) the fact that generators of new, unseen deepfake samples have not been considered when building the defense models. Our work sheds light on the need for further research and development in this field to create more robust and reliable detectors.