Adversarial collision attacks on image hashing functions

• URL: http://arxiv.org/abs/2011.09473v1
• Date: Wed, 18 Nov 2020 18:59:02 GMT
• Title: Adversarial collision attacks on image hashing functions
• Authors: Brian Dolhansky, Cristian Canton Ferrer
• Abstract summary: We show that it is possible to modify an image to produce an unrelated hash, and an exact hash collision can be produced via minuscule perturbations. In a white box setting, these collisions can be replicated across nearly every image pair and hash type. We offer several potential mitigations to gradient-based image hash attacks.
• Score: 9.391375268580806
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hashing images with a perceptual algorithm is a common approach to solving duplicate image detection problems. However, perceptual image hashing algorithms are differentiable, and are thus vulnerable to gradient-based adversarial attacks. We demonstrate that not only is it possible to modify an image to produce an unrelated hash, but an exact image hash collision between a source and target image can be produced via minuscule adversarial perturbations. In a white box setting, these collisions can be replicated across nearly every image pair and hash type (including both deep and non-learned hashes). Furthermore, by attacking points other than the output of a hashing function, an attacker can avoid having to know the details of a particular algorithm, resulting in collisions that transfer across different hash sizes or model architectures. Using these techniques, an adversary can poison the image lookup table of a duplicate image detection service, resulting in undefined or unwanted behavior. Finally, we offer several potential mitigations to gradient-based image hash attacks.

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