DefakeHop: A Light-Weight High-Performance Deepfake Detector

Hong-Shuo Chen, Mozhdeh Rouhsedaghat, Hamza Ghani, Shuowen Hu, Suya You, C.-C. Jay Kuo

Abstract: 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 uses the successive subspace learning (SSL) principle to extracts features automatically from various parts of face images. The features are extracted by channel-wise (c/w) Saab transform and further processed by our feature distillation module using spatial dimension re-duction 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. Our codes are available on GitHub <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .