  Deepfakes are a form of synthetic image generation used to generate fake
videos of individuals for malicious purposes. The resulting videos may be used
to spread misinformation, reduce trust in media, or as a form of blackmail.
These threats necessitate automated methods of deepfake video detection. This
paper investigates whether temporal information can improve the deepfake
detection performance of deep learning models.
  To investigate this, we propose a framework that classifies new and existing
approaches by their defining characteristics. These are the types of feature
extraction: automatic or manual, and the temporal relationship between frames:
dependent or independent. We apply this framework to investigate the effect of
temporal dependency on a model's deepfake detection performance.
  We find that temporal dependency produces a statistically significant (p <
0.05) increase in performance in classifying real images for the model using
automatic feature selection, demonstrating that spatio-temporal information can
increase the performance of deepfake video detection models.
