It's Not What It Looks Like: Manipulating Perceptual Hashing based Applications

Abstract

Perceptual hashing is widely used to search or match similar images for digital forensics and cybercrime study. Unfortunately, the robustness of perceptual hashing algorithms is not well understood in these contexts. In this paper, we examine the robustness of perceptual hashing and its dependent security applications both experimentally and empirically. We first develop a series of attack algorithms to subvert perceptual hashing based image search. This is done by generating attack images that effectively enlarge the hash distance to the original image while introducing minimal visual changes. To make the attack practical, we design the attack algorithms under a black-box setting, augmented with novel designs (e.g., grayscale initialization) to improve the attack efficiency and transferability. We then evaluate our attack against the standard pHash as well as its robust variant using three different datasets. After confirming the attack effectiveness experimentally, we then empirically test against real-world reverse image search engines including TinEye, Google, Microsoft Bing, and Yandex. We find that our attack is highly successful on TinEye and Bing, and is moderately successful on Google and Yandex. Based on our findings, we discuss possible countermeasures and recommendations.