  Recent advances in deep learning have enabled realistic digital alterations
to videos, known as deepfakes. This technology raises important societal
concerns regarding disinformation and authenticity, galvanizing the development
of numerous deepfake detection algorithms. At the same time, there are
significant differences between training data and in-the-wild video data, which
may undermine their practical efficacy. We simulate data corruption techniques
and examine the performance of a state-of-the-art deepfake detection algorithm
on corrupted variants of the FaceForensics++ dataset.
  While deepfake detection models are robust against video corruptions that
align with training-time augmentations, we find that they remain vulnerable to
video corruptions that simulate decreases in video quality. Indeed, in the
controversial case of the video of Gabonese President Bongo's new year address,
the algorithm, which confidently authenticates the original video, judges
highly corrupted variants of the video to be fake. Our work opens up both
technical and ethical avenues of exploration into practical deepfake detection
in global contexts.
