ID Document Forgery Detection

By reading structure instead of content. An edited ID carries regions whose compression history, noise pattern, and lighting do not match the rest of the document. A template forgery reuses identical artwork across supposedly different IDs. A fully AI-generated document carries the statistical fingerprints of the model that produced it. ScamAI scores these signals together, returns a probabilistic confidence score, and localizes the suspicious region — so a reviewer sees exactly which part of the document triggered the flag.

Yes. Fully generated documents look plausible at a glance but are structured like no issuing authority's real output — generation models leave statistical fingerprints, and synthetic documents often betray template inconsistencies a genuine document would never contain. Because new generation tools appear constantly, coverage depends on retraining: ScamAI's detection engine is updated as new document-generation techniques emerge. Results come back as confidence scores with evidence rather than a blind pass/fail, so your team controls how aggressively to act.

Route by confidence, not by binary. High-confidence forgery results can auto-decline or trigger a hard stop; mid-band flags route to human review, where the localized evidence — the tampered region highlighted on the document — lets an analyst confirm or clear the flag in seconds. Every result and its signals should land in your audit trail, so a rejected applicant, a regulator, or an internal QA review can see the concrete reason behind the decision.