Disrupting DeepFakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systems
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Face modification systems using deep learning have become increasingly powerful and accessible. Given images of a person's face, such systems can generate new images of that same person under different expressions and poses. Some systems can also modify targeted attributes such as hair color or age. This type of manipulated images and video have been coined DeepFakes. In order to prevent a malicious user from generating modified images of a person without their consent we tackle the new problem of generating adversarial attacks against image translation systems, which disrupt the resulting output image. We call this problem disrupting deepfakes. We adapt traditional adversarial attacks to our scenario. Most image translation architectures are generative models conditioned on an attribute (e.g. put a smile on this person's face). We present class transferable adversarial attacks that generalize to different classes, which means that the attacker does not need to have knowledge about the conditioning vector. In gray-box scenarios, blurring can mount a successful defense against disruption. We present a spread-spectrum adversarial attack, which evades blurring defenses.
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