A single selfie triggers four detection layers working simultaneously. Presentation attack detection catches printed photos, screens, and masks. Deepfake and GenAI detection flags synthetic faces and morphs. Presentation attack detection catches printed photos, screens, and masks. Deepfake and GenAI detection flags synthetic faces and morphs. Video injection detection blocks anything that did not originate from a live camera. All passive. No prompts, no head turns, no instructions. Easier on users. Harder on attackers.

The selfie matches the document. Microblink compares the face on the selfie to the portrait on the verified ID and return a clear, confidence-scored result. The match works across global demographics and lighting conditions, so legitimate users glide through and impostors do not.

The system spots the artifacts that GenAI leaves behind. Injection and replay detection blocks anything that did not come from the live camera.Microblink’s Fraud Lab continuously generates GenAI faces, morphs, replays, and injection variants to retrain detection models before new attack patterns reach production. By the time a threat appears in the wild, the models have already seen it.

Link a verified face to a device, ID, and optionally a payment card. That verified identity travels with the user across login, step-up checks, and high-value transactions. Organizations always know whether the same person is on the other side, not just at onboarding.

Most face checks fail because the capture itself fails. Bad light. Wrong angle. No guidance. Microblink fixes this at the source. Microblink corrects glare, blur, and framing in real time, before the photo is even taken. Users hold the phone naturally. The system does the work and people get it right on the first try. The result: a check that feels like taking a selfie, not passing a security checkpoint.