How do prosecutors and tech companies gather evidence of passive viewing of CSAM?
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Executive summary
Prosecutors and tech companies primarily rely on automated detection (hash-matching and AI classifiers), preserved provider records and digital forensics to build cases about users who viewed CSAM, including tools like PhotoDNA, proprietary video hashing (e.g., CSAI Match, PDQ) and services such as Thorn’s Safer; industry hash-matching generates the majority of reports to NCMEC (Tech Coalition, PhotoDNA, Thorn) [1] [2] [3]. Courts and commentators warn that when companies’ internal reviews or law enforcement viewing go beyond automated flags, Fourth Amendment and evidentiary limits become central battlegrounds (Wilson and 9th Circuit split) [4] [5].
1. Detection at scale: hash-matching and classifiers drive most referrals
Tech companies detect known CSAM by comparing content against databases of verified hashes—PhotoDNA, MD5, PDQ, CSAI Match and the newer perceptual/video hashes—which produces the bulk of platform referrals to NCMEC and law enforcement [2] [3] [1]. Thorn’s Safer combines multi-hash matching with machine-learning classifiers to catch novel or altered material and to prioritize what human reviewers should examine [6] [1]. The Technology Coalition reports very high uptake of these methods across industry members, with hash-matchers widely used [7].
2. From a flagged file to evidence: preservation, provider records and NCMEC workflows
Once automated systems flag content, providers remove or quarantine the material and report to the National Center for Missing & Exploited Children (NCMEC), which passes data to law enforcement; preserved service-provider records—IP logs, account metadata, payment records—are standard sources for building probable cause and warrants [8] [9]. Legislation and policy changes (e.g., REPORT Act adjustments) have increased how long providers must retain related records and broaden reporting duties, supplying investigators with the identifiers they need to pursue users [9].
3. Digital forensics and what “passive viewing” actually looks like in evidence
Digital forensic tools extract and analyze filesystem artifacts, thumbnails, caches, browser histories and cloud-stored images to show access, downloads or viewing activity; vendors and forensic teams rely on classification, image analysis and triage tools (for example, ADF, Thorn Detect, Outrider) to find and present the most probative files while minimizing manual review burden [10] [11] [12]. Forensic reports aim to show possession, access or “sought and accessed” behavior—legal elements prosecutors must prove for possession charges—so mere presence of a file is often insufficient without additional indicators of intent or access [13] [14].
4. Legal friction: private scans, government review and the Fourth Amendment split
Courts disagree over whether provider screening is a “private search” that allows downstream law enforcement review without a warrant. The Harvard Law Review analysis of United States v. Wilson shows a split: some courts treat company hashing as private and permit follow-on government review; others (notably a Ninth Circuit opinion) found that government viewing of attachments flagged by providers violated the Fourth Amendment—courts look at whether company searches were limited or expanded by later government action [5] [4]. Legal doctrine thus constrains how evidence of passive viewing is gathered and used.
5. Prosecutorial realities and the “passive viewer” problem
Research and prosecutor training acknowledge that many users who appear to “passively view” CSAM online may leave digital traces—attempted downloads, searches, timestamps—but proving knowing possession or access remains challenging when devices are shared or when logs are sparse [15] [16] [13]. Prosecutors select a narrow set of images and tie counts to unique filenames or hashes to meet proof burdens, but defense strategies routinely attack causation, access, and knowledge [14].
6. Emerging technical and policy issues: video hashing, AI CSAM and interoperability
Video hashing is newer, less interoperable and resource-intensive than still-image hashing, creating coverage gaps; multiple non‑compatible video hashing schemes mean a platform may miss known video CSAM if it lacks a particular database [3] [17]. Simultaneously, AI-generated CSAM and synthetic content complicate detection and evidentiary rules; organizations warn investigators they must adapt tools and methods to identify synthetic vs. real material and track evolving offender behavior [18] [19].
7. Limitations, disagreements and what reporting omits
Available sources document detection tools, preservation workflows and legal disputes, but do not provide a single, settled protocol for proving “passive viewing” as a standalone crime—jurisdictions, statutes, and courts differ—and sources do not enumerate a universal evidentiary checklist for prosecutors (available sources do not mention a unified standard) [13] [5]. Sources also disagree about privacy trade-offs: industry and NGOs emphasize scale and victim protection via detection [1], while courts and privacy advocates warn of constitutional limits when government relies on provider-initiated reviews [4] [5].
Bottom line: industry hash-matching plus AI triage and traditional digital forensics are the practical path from detection to evidence; legal fights over private searches and what counts as “viewing” determine whether those artifacts become admissible proof in prosecutions [2] [6] [5].