The face matches the document. Neither one is real.
A deepfake ID pairs a synthetically generated identity document with a synthetic or stolen photo - built specifically to pass automated KYC and onboarding checks. They're a fast-growing share of synthetic identity fraud, and they're built to defeat exactly the checks most onboarding stacks rely on.
The problem
Why deepfake IDs slip past identity verification
Most identity verification stacks split the problem into two checks: liveness (is a real person holding up a document right now?) and document validation (does this document look like a real ID?). Deepfake IDs are built to pass both - the photo can be a real selfie or a synthetic face, and the document is generated to mimic a genuine template closely enough to clear template-matching checks.
This is a distinct problem from face deepfakes used in liveness bypass. A deepfake ID is the document itself being synthetic - the passport, driver's licence, or national ID was never issued by any government or authority. No amount of liveness checking on the applicant's face addresses this, because the fraud is in the document, not the person presenting it.
Synthetic identity fraud often combines a deepfake ID with a thin or fabricated credit file, so the identity has no prior history to flag it as suspicious. By the time inconsistencies surface downstream, onboarding has already approved the account.
What we detect
Forensic signals that expose fraud
MRZ and security-feature inconsistency
Genuine passports and IDs encode specific check-digit and field relationships in the machine-readable zone, plus security features (holograms, microprint, UV elements) that follow strict issuer specifications. Deepfake IDs frequently get these subtly wrong.
Photo-zone boundary artifacts
Where a photo is composited into a document template, AI generation and digital splicing leave boundary artifacts - edge blending, lighting mismatches, resolution discontinuities - that TamperCheck's pixel-level analysis detects.
Issuer template mismatch
Every country and authority issues IDs against a small number of fixed layouts. TamperCheck compares submitted documents against known genuine templates for that issuer and document type, flagging structural drift.
Font and micro-print inconsistency
Government-issued IDs use specific, often security-grade typefaces and micro-print that AI generation rarely reproduces exactly. Character shape, spacing, and rendering differences are detectable forensically.
Holographic and UV simulation gaps
Physical security features that exist on genuine IDs (holograms, UV-reactive ink, kinegrams) either don't render at all in a generated image or are simulated in ways that don't match real optical behaviour under standard imaging.
Cross-field plausibility
TamperCheck checks that date of birth, issue date, expiry date, and apparent age in the photo are mutually consistent, and that field formats match the claimed issuing country's conventions.
The solution
How TamperCheck catches deepfake IDs
TamperCheck adds the forensic document layer that most KYC and identity-verification stacks are missing. It runs alongside your existing liveness and face-matching checks, focused specifically on whether the document itself is genuine.
How it works
- Dedicated deepfake and AI-generation detection layered on top of traditional ID forensics
- MRZ integrity, photo-zone boundary, and security-feature analysis built for passports, national IDs, and driver's licences
- 130+ forensic checks per document, with a plain-English verdict in about a minute
- Complements liveness and face-matching - TamperCheck checks the document, your existing stack checks the person
- 190+ issuing countries supported, with issuer-specific template matching
- Zero document storage - sensitive identity documents are processed in memory and discarded immediately
FAQ
Common questions
See it working on your documents
Start with $5 in free credits - no contract, no card required. Upload your first document and get a verdict in about a minute.