Face Poison: Obstructing DeepFakes by Disrupting Face Detection

Abstract

Recent years have seen fast development in synthesizing realistic human faces using AI-based forgery technique called DeepFake, which can be weaponized to cause negative personal and social impacts. In this work, we develop a defense method, namely FacePosion, to prevent individuals from becoming victims of DeepFake videos by sabotaging would-be training data. This is achieved by disrupting face detection, a prerequisite step to prepare victim faces for training DeepFake model. Once the training faces are wrongly extracted, the DeepFake model can not be well trained. Specifically, we propose a multi-scale feature-level adversarial attack to disrupt the intermediate features of face detectors using different scales. Extensive experiments are conducted on seven various DeepFake models using six face detection methods, empirically showing that disrupting face detectors using our method can effectively obstruct DeepFakes.