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PDQ & TMK + PDQF -- A Test Drive of Facebook's Perceptual Hashing Algorithms
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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.
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Double Down on Defense: Strengthening Deep Perceptual Hashes against Evasion Attacks without Retraining
A plug-in defense that hardens reference images and smooths match decisions reduces evasion attack success on eight deep perceptual hashes and provides a certified l2 robustness radius near 0.3.
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