Detection of AI-Manipulated Fake Faces via Mining Generalized Features

Recently, AI-manipulated face techniques have developed rapidly and constantly, which has raised new security issues in society. Although existing detection methods consider different categories of fake faces, the performance on detecting the fake faces with “unseen” manipulation techniques is still poor due to the distribution bias among cross-manipulation techniques. To solve this problem, we propose a novel framework that focuses on mining intrinsic features and further eliminating the distribution bias to improve the generalization ability. First, we focus on mining the intrinsic clues in the channel difference image (CDI) and spectrum image (SI) view of two different aspects, including the camera imaging process and the indispensable step in AI manipulation process. Then, we introduce the Octave Convolution and an attention-based fusion module to effectively and adaptively mine intrinsic features from CDI and SI view of these two different but intrinsic aspects. Finally, we design an alignment module to eliminate the bias of manipulation techniques to obtain a more generalized detection framework. We evaluate the proposed framework on four categories of fake faces datasets with the most popular and state-of-the-art manipulation techniques and achieve very competitive performances. We further conduct experiments on cross-manipulation techniques, and the results of our method show the superior advantages on improving generalization ability.