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Exploiting and Defending Against the Approximate Linearity of Apple's NeuralHash. (arXiv:2207.14258v1 [cs.CR])
July 29, 2022, 1:20 a.m. | Jagdeep Singh Bhatia, Kevin Meng
cs.CR updates on arXiv.org arxiv.org
Perceptual hashes map images with identical semantic content to the same
$n$-bit hash value, while mapping semantically-different images to different
hashes. These algorithms carry important applications in cybersecurity such as
copyright infringement detection, content fingerprinting, and surveillance.
Apple's NeuralHash is one such system that aims to detect the presence of
illegal content on users' devices without compromising consumer privacy. We
make the surprising discovery that NeuralHash is approximately linear, which
inspires the development of novel black-box attacks that can (i) …
More from arxiv.org / cs.CR updates on arXiv.org
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