Your fact-checks will appear here
The use of hash matching for detecting known CSAM
detection systems combine and and report hits with confidence scores, audit logs, and downstream human review workflows — mechanisms vendors say reduce false positives and enable reporting to authorit...
Platforms combine signature-based matching and machine learning to separate illegal child sexual abuse material (CSAM) from broader categories like child erotica, but each method has brittle edges: ha...
Current and perceptual/hash‑matching tools form complementary lines of defense: hashing reliably identifies previously documented with very low false positives but fails on “new” or synthetically gene...
Accidental or momentary access to a hidden-service page that contains child sexual abuse material (CSAM) does not automatically equate to criminal liability; common legal defenses include lack of inte...
Automated hash‑matching systems detect known child sexual abuse material (CSAM) by converting media into compact "hashes" and comparing them to databases of verified CSAM hashes, enabling fast, scalab...