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A light-weight high-performance Deepfake detection method, called DefakeHop,is proposed in this work. State-of-the-art Deepfake detection methods are builtupon deep neural networks. DefakeHop extracts features automatically using thesuccessive subspace learning (SSL) principle from various parts of face images.The features are extracted by c/w Saab transform and further processed by ourfeature distillation module using spatial dimension reduction and softclassification for each channel to get a more concise description of the face.Extensive experiments are conducted to demonstrate the effectiveness of theproposed DefakeHop method. With a small model size of 42,845 parameters,DefakeHop achieves state-of-the-art performance with the area under the ROCcurve (AUC) of 100%, 94.95%, and 90.56% on UADFV, Celeb-DF v1 and Celeb-DF v2datasets, respectively.

DefakeHop: A Light-Weight High-Performance Deepfake Detector