Research Output
Hamming Distributions of Popular Perceptual Hashing Techniques
  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.

  • Type:


  • Date:

    28 November 2022

  • Publication Status:


  • Funders:

    Cyan Forensics Ltd


McKeown, S., & Buchanan, W. J. (in press). Hamming Distributions of Popular Perceptual Hashing Techniques. Forensic Science International: Digital Investigation,



Perceptual Hashing; Fuzzy Hashing; Hash Matching; CSAM; Image Forensics

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