Content-based file matching has been widely deployed for decades, largely for the detection of sources of copyright infringement, extremist materials, and abusive sexual media. Perceptual hashes, such as Microsoft's PhotoDNA, are one automated mechanism for facilitating detection, allowing for machines to approximately match visual features of an image or video in a robust manner. However, there does not appear to be much public evaluation of such approaches, particularly when it comes to how effective they are against content-preserving modifications to media files. In this paper, we present a million-image scale evaluation of several perceptual hashing archetypes for popular algorithms (including Facebook's PDQ, Apple's Neuralhash, and the popular pHash library) against seven image variants. The focal point is the distribution of Hamming distance scores between both unrelated images and image variants to better understand the problems faced by each approach.

翻译：几十年来,基于内容的文档匹配被广泛采用,主要用于发现侵犯版权、极端主义材料和滥用性媒体的来源。像微软的PhotoDNA这样的概念性错位是便利检测的一种自动化机制,使机器能够以稳健的方式大致匹配图像或视频的视觉特征。然而,似乎没有多少公众对这种方法的评价,特别是当它们对于修改媒体文件内容的有效性时。在本文中,我们对几种流行算法(包括Facebook的PDQ、苹果的Neuralhash和流行的pHash图书馆)的观念性散射成型进行了百万倍规模的评价,以更好地了解每种方法所面临的问题。