Featured module
Device Signals — liveness & presentation-attack detection
Detection for spoofed presentations — printed photos, screen replays, and masks — plus liveness failures, caught at the point of capture.
- Catches printed, replayed, and masked spoofs
- Confirms a live, present human at capture
- Runs before downstream deepfake and document checks
What it does
Confirms the face at capture is a live, present person, not a printed photo, a screen replay, or a mask held to the lens. Spoofed presentations are flagged as the media is captured, before the frame enters your pipeline.
Where it fits
The capture step of IDV: signup selfies, document photos, and liveness video, checked as the media is captured.
The earliest line of defense
Running at capture means a spoofed or replayed presentation is caught before any downstream check even sees the frame, stopping the attack earlier, and cheaper, than catching it later.
Layering liveness into an IDV stack
Liveness is one layer, not the whole defense, so pair it with deepfake and document detection: an attack that beats one check still has to beat the others. A failed check can trigger a retake or step-up instantly, because it's a fact about the capture, not a judgment about the person.
/ROI
What this is worth
- One AI engine for every surface — add modules without adding vendors, contracts, or review queues.
- Evidence-backed results drop straight into your decisioning: auto-decline, step-up, or route to review.
- Live in a day: one REST API call in, a scored result out — no model training, no data-science team required.
Common questions
What is a presentation attack?
A presentation attack presents a fake face to a real camera to pass as a live person: a printed photo, a video replayed on a screen, or a mask worn or held to the lens. It is the classic way naive selfie and liveness checks are defeated — the camera is real, but the thing in front of it is not a live subject. Liveness detection targets exactly this, reading the captured image for the signals a screen, print, or mask leaves behind.
How does liveness detection work?
By examining the captured face rather than trusting the session. Detection reads the signals a spoof leaves — screen glare and moiré, print texture and edges, and the depth and micro-motion a living face shows that a photo or mask does not — and returns a scored signal alongside the media. Your flow can reject or step up a session that cannot demonstrate a live, present subject before the frames reach downstream analysis.
How does this work with deepfake detection?
They are complementary layers. Liveness confirms a real, present human is in front of the camera; deepfake detection judges whether the face itself has been synthetically generated or swapped. Run together, an attack has to defeat both — a mask that survives a quick look still fails liveness, and a swapped face that looks live still fails deepfake detection. ScamAI returns each as its own scored signal with the result, so the decision comes with its reasons attached.
See ScamAI on Device Signals — liveness & presentation-attack detection
15 minutes, on your own media. Pick a slot and leave with a result.