Short answer
ai images identity verification is not a content moderation problem wearing a different hat. In moderation, a synthetic image is the harm. In identity checks, the synthetic image is the instrument, and the harm is a record that should never have been created. That distinction changes what you check, when you check it, and what counts as a pass.
The case that shows the pattern
In October 2026, FactCheckHub reported that Nigeria's electoral commission was investigating suspected AI-generated photographs in a preliminary voter register. The interesting part is not that synthetic portraits appeared. It is that they passed whatever intake process existed and became part of a government record before anyone examined them. Detection at that point is forensics after the fact.
Why the intake model is the weak point
Most identity pipelines are built to answer one question: does this document and face belong to a real person who matches the claim. They are usually good at catching reuse, because the same face appearing under two identities is easy to flag. They are much weaker against a face that was never real to begin with, because there is nothing to match it against. A generated portrait has no prior record to conflict with.
What actually catches generated portraits
- Liveness at capture: a still image cannot prove presence. Where the pipeline accepts an upload rather than a live capture, this is the single biggest gap.
- Consistency across the document set: a portrait, a document photo, and a selfie should agree on lighting, geometry, and capture conditions. Generated portraits often look correct individually and inconsistent as a set.
- Metadata continuity: capture device, timestamp, and processing chain should describe one event. A portrait with no capture record in a pipeline that expects one is worth a second look, though absence alone proves nothing since compression strips this routinely.
- Volume patterns: generated identities tend to arrive in batches from a narrow set of sources, on a similar schedule, with similar quality characteristics.
Where detection helps and where it misleads
A detector can flag portraits that do not resemble camera originals, which is useful for sampling and for prioritising a queue. It cannot confirm that a person does not exist. A high score is not proof of fabrication, and a low score is not proof of a real person, since a photograph of a photograph or a heavily processed legitimate portrait both score unpredictably. Where the score conflicts with a liveness result, escalate to human review rather than letting either one decide.
What to record before a record is created
- Whether the capture was live or uploaded, and which component enforced it.
- The full document set as received, kept together, since consistency checks depend on the set and not on any single file.
- Which check produced the rejection or the pass, so a later audit can reconstruct the decision.
- The batch context: how many other submissions arrived from the same source in the same window.
What this workflow cannot do
It cannot establish that a person exists. It cannot recover evidence destroyed by recompression before submission. And it cannot substitute for a legal determination about what standard of proof a register or account requires, which varies by jurisdiction and by the consequences of a false accept. Read the methodology for how the layers combine, or check sample reports to see how a case with an incomplete capture record is documented.
If you need to inspect provenance and metadata on a submitted portrait before approving it, start a free review and record the result alongside the decision.