  A light-weight high-performance Deepfake detection method, called DefakeHop,
is proposed in this work. State-of-the-art Deepfake detection methods are built
upon deep neural networks. DefakeHop extracts features automatically using the
successive subspace learning (SSL) principle from various parts of face images.
The features are extracted by c/w Saab transform and further processed by our
feature distillation module using spatial dimension reduction and soft
classification for each channel to get a more concise description of the face.
Extensive experiments are conducted to demonstrate the effectiveness of the
proposed DefakeHop method. With a small model size of 42,845 parameters,
DefakeHop achieves state-of-the-art performance with the area under the ROC
curve (AUC) of 100%, 94.95%, and 90.56% on UADFV, Celeb-DF v1 and Celeb-DF v2
datasets, respectively.
