Robustness of Practical Perceptual Hashing Algorithms to Hash-Evasion and Hash-Inversion Attacks

Jordan Madden, Moxanki Bhavsar, Lhamo Dorje, Xiaohua Li

Keywords: Perceptual Hashing, Adversarial Machine Learning, Robustness, Security

TL;DR: This paper conducted a systematic evaluation of the adversarial robustness of some major perceptual hashing algorithm that have been used widely in practice.

Abstract: Perceptual hashing algorithms (PHAs) are widely used for identifying illegal online content and are thus integral to various sensitive applications. However, due to their hasty deployment in real-world scenarios, their adversarial security has not been thoroughly evaluated. This paper assesses the security of three widely utilized PHAs—PhotoDNA, PDQ, and NeuralHash—against hash-evasion and hash-inversion attacks. Contrary to existing literature, our findings indicate that these PHAs demonstrate significant robustness against such attacks. We provide an explanation for these differing results, highlighting that the inherent robustness is partially due to the random hash variations characteristic of PHAs. Additionally, we propose a defense method that enhances security by intentionally introducing perturbations into the hashes.

Submission Number: 21