  AI-created face-swap videos, commonly known as Deepfakes, have attracted wide
attention as powerful impersonation attacks. Existing research on Deepfakes
mostly focuses on binary detection to distinguish between real and fake videos.
However, it is also important to determine the specific generation model for a
fake video, which can help attribute it to the source for forensic
investigation. In this paper, we fill this gap by studying the model
attribution problem of Deepfake videos. We first introduce a new dataset with
DeepFakes from Different Models (DFDM) based on several Autoencoder models.
Specifically, five generation models with variations in encoder, decoder,
intermediate layer, input resolution, and compression ratio have been used to
generate a total of 6,450 Deepfake videos based on the same input. Then we take
Deepfakes model attribution as a multiclass classification task and propose a
spatial and temporal attention based method to explore the differences among
Deepfakes in the new dataset. Experimental evaluation shows that most existing
Deepfakes detection methods failed in Deepfakes model attribution, while the
proposed method achieved over 70% accuracy on the high-quality DFDM dataset.
