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.

本文提出了一种轻量、高性能的 Deepfake 检测方法 DefakeHop，使用连续子空间学习原理从面部图像的各个部分自动提取特征，并通过特征精炼模块进行进一步处理以获得更简洁的面部描述，最终在 UADFV、Celeb-DF v1 和 Celeb-DF v2 数据集上实现了 ROC 曲线下面积（AUC）分别为 100％、94.95％ 和 90.56％ 的最新性能。

DefakeHop: 一种轻量高性能的Deepfake检测器