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Transparency around DHS and ICE procurement of surveillance tools is real but highly uneven: procurement notices, inspector‑general reports, and civil society FOIA wins have revealed major purchases a...
Courts treat chat logs and messaging exports as powerful but technically fraught evidence: admissibility turns on chain-of-custody, forensic soundness, and whether providers or tools acted as governme...
ICE’s biggest active contracts for phone‑forensics and related surveillance tools as of early 2026 are concentrated among a handful of specialized firms: Cellebrite (major universal forensic extractio...
Defendants in CSAM cases commonly invoke technical and factual defenses such as “unknowing possession” due to malware or unsolicited downloads, mislabeling or misidentification of images, and challeng...
Digital forensics uses multiple, converging techniques—file hashing and fuzzy hashing, on-device/client-side matching, artifact timeline and metadata analysis, cloud-account vouchers and reporting, an...
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...
The Child Rescue Coalition’s Child Protection System (CPS) does analyze chatroom content in at least two documented ways: by capturing and indexing public chatroom messages to detect grooming and by p...
A reliable browser download signal is a constellation of filesystem and application artifacts rather than a single file: on Windows the strongest indicators are browser-specific databases (notably the...
Courts and prosecutors have accepted defenses based on lack of knowledge, malware involvement, and the distinction between real and AI-generated images, but available reporting shows few publicly docu...
Defenses against malware or automated processes that save CSAM to a device fall into two broad categories: technical prevention/detection across endpoints and investigatory/forensic strategies to expl...
Forensic teams use a mix of image-authentication algorithms, metadata and provenance checks, hash-matching, facial-similarity tools and human-led investigative context to try to attribute or debunk AI...