  The increasing popularity of facial manipulation (Deepfakes) and synthetic
face creation raises the need to develop robust forgery detection solutions.
Crucially, most work in this domain assume that the Deepfakes in the test set
come from the same Deepfake algorithms that were used for training the network.
This is not how things work in practice. Instead, we consider the case where
the network is trained on one Deepfake algorithm, and tested on Deepfakes
generated by another algorithm. Typically, supervised techniques follow a
pipeline of visual feature extraction from a deep backbone, followed by a
binary classification head. Instead, our algorithm aggregates features
extracted across all layers of one backbone network to detect a fake. We
evaluate our approach on two domains of interest - Deepfake detection and
Synthetic image detection, and find that we achieve SOTA results.
