Who should use the Detect liveness workflow?
Teams or solo builders working on security & privacy tasks who want a repeatable process instead of one-off tool experiments.
Execution Map
2-step pipeline — follow each step in order
1Core · Step 1 of 2
Open task pageDetect Liveness
Use NtechLab (FindFace Multi) to analyze a real-time video or image to confirm the subject is a live person, preventing spoof attacks with motion and texture analysis.
Why this matters
This is the primary step where liveness is verified, forming the foundation of the entire workflow.
What you get
A liveness score or binary result indicating whether the subject is live.
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2Refine · Step 2 of 2
Open task pageQuality Validation: Detect Deepfakes
Apply the DeepFakeDetectionChallengeTestSetV3 to verify that the captured media has not been algorithmically generated or manipulated, ensuring the liveness detection is not deceived.
Why this matters
Adds a critical security layer to defend against sophisticated AI-based spoofing attacks.
What you get
Confirmation that the media is authentic and not a deepfake, improving trust in the liveness result.
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