Keep Factually independent
Whether you agree or disagree with our analysis, these conversations matter for democracy. We don't take money from political groups - even a $5 donation helps us keep it that way.
What technologies are used to combat CSAM distribution online?
Executive summary
Platforms and safety vendors use a mix of hash-matching (cryptographic and perceptual), machine‑learning classifiers for unknown/novel CSAM, text analysis for grooming/sextortion, and platform-level workflows to triage and report material to authorities; hash methods account for the majority of automated identifications while newer AI classifiers aim to find unseen or AI‑generated CSAM [1] [2] [3]. Key vendors and tools cited in industry reporting include PhotoDNA, MD5, PDQ, CSAI Match, Thorn’s Safer (including perceptual and cryptographic hashing plus a CSAM classifier), and commercial offerings for unknown-CSAM detection such as Roke Vigil AI CAID [1] [4] [2] [3].
1. Hash-matching: the backbone that finds “known” CSAM
Hash matching creates a compact “fingerprint” of an image or selected video frames so services can rapidly block or flag previously identified CSAM. PhotoDNA — developed by Microsoft and donated to NCMEC — is presented as an industry standard for larger platforms and is available as a cloud service for smaller operators [5]. Industry coalitions list PhotoDNA alongside MD5, PDQ and CSAI Match as the tools that generate “the vast majority of CSAM identification and reports” to organizations such as NCMEC [1]. Thorn and other providers use both cryptographic and perceptual hashing to detect exact matches and altered variants of known images or frames within videos [4] [2].
2. Perceptual hashing and video hashing: matching altered or embedded content
Perceptual hashing compares similarity rather than strict binary equality, allowing detection of edited or slightly altered images and frames extracted from videos; Thorn’s SSVH (scene‑based video hashing) builds video hashes from perceptual hashes of visually distinct scenes [6] [4]. The capability to hash video frames individually — and to match them against databases — is explicitly used to identify CSAM even when content is edited or embedded into longer media [1] [6].
3. AI classifiers for “unknown” and AI‑generated CSAM
Hashing only detects content already catalogued. To find never‑seen or AI‑generated CSAM, platforms and vendors increasingly deploy machine‑learning classifiers that analyze thousands of visual attributes to predict whether content may be abusive; Thorn’s Safer Predict and Roke Vigil AI CAID are examples marketed to identify novel CSAM at scale [2] [3]. Industry commentary frames these classifiers as necessary to address emerging threats such as AI‑generated material, but their adoption raises questions about precision, false positives and the need for human review [2] [3] [7].
4. Text analysis and behavior detection for grooming and sextortion
Beyond images and video, platforms use natural language processing and on‑device or server‑side machine learning to flag sexualized conversations, grooming attempts, and sextortion—areas where hash matching is irrelevant. Reports note that text‑based exploitation detection helps identify conversations related to CSAM and grooming, but automated flags require human verification given limits on precision and context sensitivity [6] [7].
5. On‑device detection, privacy tradeoffs, and regulatory pressure
Some approaches (notably Apple’s earlier proposal) perform on‑device matching or use on‑device ML to analyze images before upload — a design intended to preserve privacy while detecting known CSAM — but on‑device scanning has drawn privacy and civil‑liberties debate and has been revised in some proposals [8] [9] [10]. Regulators (for example in the UK) are consulting on measures that may push platforms toward proactive detection of unknown CSAM, intensifying the technical and ethical tradeoffs that companies and civil‑society actors are weighing [3] [10].
6. Operational pipelines: triage, reporting, and human oversight
Detection technologies are typically paired with moderation UIs, triage systems, and reporting workflows that connect platforms to NCMEC or national hotlines; providers emphasize that human analysts must verify automated flags before legal classification or law‑enforcement action [2] [1] [7]. Hotlines and coalitions like INHOPE and the Child Rescue Coalition combine automated scanning with human-led analysis to prioritize investigations and identify perpetrators [11].
7. Limitations, competing viewpoints and hidden incentives
Available reporting highlights tradeoffs: hashing is efficient and low‑risk for privacy but only finds known files; AI classifiers can find new material but risk false positives and potential harms if misapplied [1] [7]. Vendor materials (Thorn, Resolver, Safer) understandably promote their products’ strengths — including perceptual hashing and predictive AI — while industry‑facing articles emphasize that large platforms have an incentive to outsource detection for compliance and reputational reasons [4] [3] [2]. Independent scientists and civil‑society commentators have repeatedly warned that mandatory on‑device scanning and broad automated surveillance could erode privacy and produce large numbers of false alerts that hamper investigations [9] [10].
8. Bottom line for readers and policymakers
The technological toolkit is mature on “known” CSAM (hash matching) and rapidly evolving on “unknown” CSAM (AI classifiers, perceptual/video hashing, text analysis). Policymakers must balance faster detection and victim protection against accuracy limits, privacy implications and the commercial incentives of vendors — and all automated flags still require human verification before legal action [1] [2] [7]. Available sources do not mention independent quantitative error rates for specific commercial classifiers in operational settings; those performance details are not reported in the provided documents (not found in current reporting).