  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.
