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There is broad, well-documented use of browser fingerprinting by advertisers, fraud teams and some law‑enforcement partners to link online sessions to persistent browser profiles , but the sources pro...
Investigators cannot rely on a single test to prove whether alleged child sexual abuse material (CSAM) was produced by an LLM or by a human; they use a layered forensic process that combines metadata ...
Hash values (cryptographic and perceptual) are the primary tool investigators and platforms use to detect and triage known CSAM quickly; databases such as NCMEC’s and aggregated pools reportedly conta...
Tech companies and law enforcement primarily find people who view or host CSAM by matching content against known-hash databases (PhotoDNA, PDQ, TMK and others) and by using AI classifiers to flag nove...
Investigators can establish access to CSAM without a suspect’s physical device by tracing cloud-stored matches, server logs, account metadata, and financial or network traces — for example, on-device ...
Advances in hashing and AI have dramatically increased platforms’ ability to find and remove “known” CSAM at scale—industry tools processed billions of images and surfaced millions of matches in recen...
Regulators, independent researchers, standards bodies and vendors are converging on a picture of validation and explainability for automated CSAM detection that treats accuracy as necessary but not su...
Platforms rely on a layered technical playbook to find AI‑generated CSAM: robust hash‑matching for known material, machine‑learning classifiers tuned to spot sexual content and estimate age, and orche...
Honeypots capture network traffic and interactions that investigators can link to an online user using conventional network identifiers (IP addresses), device- and browser-based fingerprints, and even...
Digital investigators use a mix of device-level extraction, hash-based content matching, network/IP correlation, cloud-provider telemetry and triage/AI tools to trace material and accounts involved in...