Squint Hard Enough: Attacking Perceptual Hashing with Adversarial Machine Learning
Jonathan Prokos, Neil Fendley, Matthew Green, Roei Schuster, Eran Tromer, Tushar M. Jois, Yinzhi Cao
摘要
Many online communications systems use perceptual hash matching systems to detect illicit files in user content. These systems employ specialized perceptual hash functions such as Microsoft's PhotoDNA or Facebook's PDQ to produce a compact digest of an image file that can be approximately compared to a database of known illicit-content digests. Recently, several proposals have suggested that hash-based matching systems be incorporated into client-side and end-to-end encrypted (E2EE) systems: in these designs, files that register as illicit content will be reported to the provider, while the remaining content will be sent confidentially. By using perceptual hashing to determine confidentiality guarantees, this new setting significantly changes the function of existing perceptual hashing -thus motivating the need to evaluate these functions from an adversarial perspective, using their perceptual capabilities against them. For example, an attacker may attempt to trigger a match on innocuous, but politicallycharged, content in an attempt to stifle speech. In this work we develop threat models for perceptual hashing algorithms in an adversarial setting, and present attacks against the two most widely deployed algorithms: PhotoDNA and PDQ. Our results show that it is possible to efficiently generate targeted second-preimage attacks in which an attacker creates a variant of some source image that matches some target digest. As a complement to this main result, we also further investigate the production of images that facilitate detection avoidance attacks, continuing a recent investigation of Jain et al. Our work shows that existing perceptual hash functions are likely insufficiently robust to survive attacks on this new setting. * Currently affiliated with Two Six Technologies, LLC (Arlington, VA). details of some widely-used hashing algorithms, most notably Microsoft's PhotoDNA, are kept tightly guarded and are available only under strict confidentiality agreements [32, 39] .
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引用它的顶会 Paper7
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- Eva: Efficient Privacy-Preserving Proof of Authenticity for Lossily Encoded VideosChengru Zhang, Xiao Yang, David F. Oswald, Mark Ryan 等S&P 2025
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