Restricted reference database distributing matching signals across participating platforms
GIFCT Hash-Sharing Database
The GIFCT Hash-Sharing Database is a shared collection of content fingerprints and associated labels used to help participating services identify qualifying terrorist and violent extremist material. Members contribute signals, and GIFCT can also create hashes. Access and inclusion are governed separately from a platform's own enforcement decisions.
Admission filters and withdrawal visibility
The published taxonomy describes separate designation, qualifying-incident and behavioural routes. TCAP-sourced inputs must also satisfy GIFCT's parameters rather than entering automatically. GIFCT's transparency accounts report label corrections, hash removals and a nonviolent music-video removal. A contributor withdrawal becomes visible to another member at its next access, but that does not demonstrate the recipient processed it or revisited an earlier action. The correction examples are not a random database sample and cannot yield a database-wide error rate.
UN list, qualifying incident and behavioral routes have separate predicates. Behavioral classification needs more than an ideological label. December 2022 terminology remains dated.
GIFCT Hash-Sharing Database → GIFCT member technology companies
supplies matching information while platforms retain decisions
Cryptographic/perceptual signals assist identification. GIFCT says it does not dictate enforcement; actual provider configuration, review and outcome need separate records.
GIFCT member technology companies → GIFCT Hash-Sharing Database
contribute qualified hashes and labels
Member-supplied signals and feedback are documented; GIFCT also can create hashes. Admission under taxonomy is distinct from a platform moderation verdict.
GIFCT member technology companies → GIFCT Hash-Sharing Database
reported correcting labels and removing hashes
Dated 2022/2025 report accounts, not a verified universal correction service-level agreement.
2022 account describes review/sample corrections and a separate nonviolent music-video removal. Another member sees contributor withdrawal at its next access. Nonrandom feedback does not establish a database-wide error rate; no recipient processing or restoration proved.
2022 report says GIFCT took full management and rescinded access from five legacy nonmembers that did not pursue membership. Historical organizational access change, not current membership count or content removal.
2022 report says GIFCT took full management and rescinded access from five legacy nonmembers that did not pursue membership. Historical organizational access change, not current membership count or content removal.
Member-supplied signals and feedback are documented; GIFCT also can create hashes. Admission under taxonomy is distinct from a platform moderation verdict.
UN list, qualifying incident and behavioral routes have separate predicates. Behavioral classification needs more than an ideological label. December 2022 terminology remains dated.
Cryptographic/perceptual signals assist identification. GIFCT says it does not dictate enforcement; actual provider configuration, review and outcome need separate records.
Dated 2022/2025 report accounts, not a verified universal correction service-level agreement.
Reported by the cited source
2022 account describes review/sample corrections and a separate nonviolent music-video removal. Another member sees contributor withdrawal at its next access. Nonrandom feedback does not establish a database-wide error rate; no recipient processing or restoration proved.