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.
@article{arxiv.2103.06929,
title = {DefakeHop: A Light-Weight High-Performance Deepfake Detector},
author = {Hong-Shuo Chen and Mozhdeh Rouhsedaghat and Hamza Ghani and Shuowen Hu and Suya You and C. -C. Jay Kuo},
journal= {arXiv preprint arXiv:2103.06929},
year = {2021}
}