# PDQ & TMK + PDQF -- A Test Drive of Facebook's Perceptual Hashing Algorithms

arXiv: https://arxiv.org/abs/1912.07745 · HTML: https://mlsanity.com/paper/1912.07745/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2019.

## Built on by (most cited first, 4 of 4)

- [Stateful Defenses for Machine Learning Models Are Not Yet Secure Against Black-box Attacks](https://mlsanity.com/paper/2303.06280/) (2023)
- [Do You See What I See? Capabilities and Limits of Automated Multimedia Content Analysis](https://mlsanity.com/paper/2201.11105/) (2021)
- [Re-purposing Perceptual Hashing based Client Side Scanning for Physical Surveillance](https://mlsanity.com/paper/2212.04107/) (2022)
- [Double Down on Defense: Strengthening Deep Perceptual Hashes against Evasion Attacks without Retraining](https://mlsanity.com/paper/2608.03101/) (2026)

## 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.