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How do hashing techniques like PhotoDNA and perceptual hashes detect known CSAM?
Executive summary
Hash-based systems detect "known" CSAM by converting images/videos into compact signatures and comparing them to curated databases of verified CSAM hashes; PhotoDNA and other perceptual hashes can match files even after simple edits like resizing or re-encoding (e.g., PhotoDNA, PDQ, NeuralHash) [1] [2] [3]. These systems are highly effective at blocking re‑shared material at scale, but they cannot by themselves find novel abuse content and have trade‑offs around false positives, forgery risks and access to sensitive hash lists [4] [5] [6].
1. How hash matching actually works: fingerprints, not pictures
Hash matching turns an image or video into a compact numeric "fingerprint" (a hash) that represents visual features; services compare that fingerprint to databases of hashes for material already identified as CSAM and take action when a match occurs (PhotoDNA’s hashes and matching workflow is Microsoft’s stated approach) [1] [7]. Cryptographic hashes (e.g., MD5) identify exact file duplicates, while perceptual hashes encode visual features so slightly altered copies still match — PhotoDNA, Meta’s PDQ family, Apple’s NeuralHash, and Safer/Thorn tools are examples of perceptual approaches [2] [3] [8] [9].
2. PhotoDNA and industry adoption: a de facto standard for known content
Microsoft developed PhotoDNA (with Dartmouth’s Hany Farid) and donated it for wider use; it became widely adopted by large platforms and hotlines and is available via cloud or through partner programs, making it a primary tool for removing previously identified CSAM at scale [1] [2] [10]. Industry groups report broad voluntary uptake: survey data indicate many tech companies use at least one image hash matcher, with PhotoDNA explicitly credited for detecting and reporting millions of known abuse images [7] [11].
3. Strengths: scale, low operational burden, and preventing revictimization
Hash matching scales to millions of uploads in real time and automatically flags duplicates so platforms and law enforcement can remove and report repeat material quickly, reducing revictimization of victims and cutting the workload for human reviewers [12] [13] [1]. Perceptual hashes’ tolerance for common manipulations — resizing, recompression, minor color edits — makes them well suited to catching recycled material that would evade simple cryptographic checks [14] [3] [2].
4. What hashing cannot do: novel content and contextual judgement
All sources emphasize a fundamental limitation: hash systems only detect "known" CSAM whose hashes already exist in curated databases; they cannot identify previously unseen material on their own, so classifiers or investigator work remain necessary to find new abuse content [4] [5] [12]. Industry players therefore pair hashing with AI classifiers or human review to surface novel or ambiguous content [5] [8].
5. Accuracy, false positives, and operational settings
Published analyses and industry statements report low false positive rates for mature systems — for example, claims about PhotoDNA’s very low false positive rates have circulated (one source cites 1 in 50 billion) — but researchers and implementers stress that thresholds and handling policies matter and that human verification remains part of the workflow [4] [15]. Platforms can tune matching sensitivity and post‑match workflows (block, quarantine, human review) depending on risk tolerance and resources [16].
6. Security and adversarial concerns: forgery and inversion risks
Perceptual hashing trades strict cryptographic collision resistance for robustness to edits, which makes it possible in theory to craft images that either evade detection or, if the algorithm or hashes are known, forge collisions to implicate innocent images. Research has shown practical attacks (inversion, poisoning) against several perceptual hash algorithms, underscoring security tradeoffs and the risks of revealing algorithm details or hash lists [6] [17]. Some providers restrict access to algorithm internals and hash databases to limit those risks [18].
7. Access to hash databases and governance questions
Databases of verified CSAM hashes are controlled by entities such as NCMEC, Thorn (Safer), and other coalitions; access is gated for legal, privacy, and security reasons, which helps prevent misuse but can make adoption harder for small platforms [18] [2] [7]. That governance creates both protective and exclusionary effects: it reduces chances of algorithm abuse but limits which organisations can participate in hash‑matching ecosystems [18] [2].
8. The practical ecosystem: combined approaches win
Industry and nonprofit sources describe a multipronged strategy: perceptual and cryptographic hashing for known material, AI classifiers to surface novel content, and human review and law enforcement reporting to verify and act — an ecosystem approach that recognizes the limits of any single tool [5] [8] [19]. The consensus among providers is that hashing is indispensable for stopping recycling of known CSAM, but it must be combined with other technical and policy measures to address new content and prevent errors [5] [4].
Limitations and gaps: available sources do not provide full technical details of proprietary hash algorithms or exhaustive independent benchmarking across all products; notable security research has published attacks on perceptual hashes but public quantitative comparisons vary between vendors and studies [17] [20].