PDQ & TMK + PDQF -- A Test Drive of Facebook's Perceptual Hashing Algorithms
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Abstract (arXiv)
Efficient and reliable automated detection of modified image and multimedia files has long been a challenge for law enforcement, compounded by the harm caused by repeated exposure to psychologically harmful materials. In August 2019 Facebook open-sourced their PDQ and TMK + PDQF algorithms for image and video similarity measurement, respectively. In this report, we review the algorithms' performance on detecting commonly encountered transformations on real-world case data, sourced from contemporary investigations. We also provide a reference implementation to demonstrate the potential application and integration of such algorithms within existing law enforcement systems.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Dec 2019.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 0; 2025: 0; 2026: 1.
4 papers.
- 2023Stateful Defenses for Machine Learning Models Are Not Yet Secure Against Black-box Attackscited by 11
- 2021Do You See What I See? Capabilities and Limits of Automated Multimedia Content Analysiscited by 2
- 2022Re-purposing Perceptual Hashing based Client Side Scanning for Physical Surveillancecited by 0
- 2026Double Down on Defense: Strengthening Deep Perceptual Hashes against Evasion Attacks without Retrainingcited by 0
- 2026Double Down on Defense: Strengthening Deep Perceptual Hashes against Evasion Attacks without Retraining4 Aug 2026
- 2023Stateful Defenses for Machine Learning Models Are Not Yet Secure Against Black-box Attacks11 Mar 2023
- 2022Re-purposing Perceptual Hashing based Client Side Scanning for Physical Surveillance8 Dec 2022
- 2021Do You See What I See? Capabilities and Limits of Automated Multimedia Content Analysis15 Dec 2021