Perceptual hashing algorithms (PHAs) are utilized extensively for identifying
illegal online content. Given their crucial role in sensitive applications,
understanding their security strengths and weaknesses is critical. This paper
compares three major PHAs deployed widely in practice: PhotoDNA, PDQ, and
NeuralHash, and assesses their robustness against three typical attacks: normal
image editing attacks, malicious adversarial attacks, and hash inversion
attacks. Contrary to prevailing studies, this paper reveals that these PHAs
exhibit resilience to black-box adversarial attacks when realistic constraints
regarding the distortion and query budget are applied, attributed to the unique
property of random hash variations. Moreover, this paper illustrates that
original images can be reconstructed from the hash bits, raising significant
privacy concerns. By comprehensively exposing their security vulnerabilities,
this paper contributes to the ongoing efforts aimed at enhancing the security
of PHAs for effective deployment.

通过对普遍应用的三种主要感知哈希算法 ——PhotoDNA、PDQ 和 NeuralHash 的比较，并评估它们在正常图像编辑攻击、恶意对抗攻击和哈希逆转攻击等三种典型攻击下的鲁棒性，揭示了这些感知哈希算法在存在真实约束条件下显示出对黑箱对抗攻击的弹性，并且可以通过哈希位重新构建原始图像，从而引发了重大的隐私问题。通过全面揭示其安全漏洞，本文为增强感知哈希算法的安全性以实现有效的部署作出了贡献。