MetaFake: Few-shot Face Forgery Detection with Meta Learning

Abstract

With remarkable progress achieved by facial forgery technologies, their potential security risks cause serious concern to society since they can easily fool face recognition systems and even human beings. Current forgery detection methods have achieved excellent performance when training with a large-scale database. However, they usually fail to give correct predictions in real applications where only a few fake samples created by unseen forgery methods are available. In this paper, we propose a novel method to boost the performance of identifying samples generated by unseen techniques, dubbed MetaFake, which requires only a few fake samples. Our MetaFake enjoys the part features located by meta forgery prototypes created adaptively based on each task. The local-aggregated module helps to integrate these part features for the final prediction. Besides, we establish a large database of about 0.6 million images to verify the proposed method, including fake samples synthesized by 18 forgery techniques. Extensive experiments demonstrate the superior performance of the proposed method.