Use case — Fraud

Deepfake and injection defense

Separate a live human from a replayed or synthetic one.

The problem

Generated faces and virtual cameras defeat naive selfie checks, and the attack surface changes every quarter.

Modules used

Passive liveness

Active liveness

Device and IP analysis

The flow

What runs, in order

Each step is an independently configurable module with its own result on the decision object.

1
Step 1
Passive liveness with presentation-attack signals on every session
2
Step 2
Active liveness challenge where risk warrants it
3
Step 3
Device integrity, emulator and virtual-camera signals attached to the decision

Outcome

Per-signal spoofing evidence on the session, not a single opaque pass or fail.

Catch the same face applying under different names.

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