  Applications of deep learning to synthetic media generation allow the
creation of convincing forgeries, called DeepFakes, with limited technical
expertise. DeepFake detection is an increasingly active research area. In this
paper, we analyze an existing DeepFake detection technique based on head pose
estimation, which can be applied when fake images are generated with an
autoencoder-based face swap. Existing literature suggests that this method is
an effective DeepFake detector, and its motivating principles are attractively
simple. With an eye towards using these principles to develop new DeepFake
detectors, we conduct a reproducibility study of the existing method. We
conclude that its merits are dramatically overstated, despite its celebrated
status. By investigating this discrepancy we uncover a number of important and
generalizable insights related to facial landmark detection, identity-agnostic
head pose estimation, and algorithmic bias in DeepFake detectors. Our results
correct the current literature's perception of state of the art performance for
DeepFake detection.
