Deepfake Detection

FaceSign detects deepfakes, face swaps, and presentation attacks at a rate of 99% or higher, matching the iBeta PAD Level 2 standard for presentation attack detection. This page explains how the system achieves that rate and why interactive liveness analysis defeats attacks that fool static checks.

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Why static liveness checks fail

Traditional liveness detection asks the user to take a selfie or hold still for a moment. These approaches are vulnerable to:

• Pre-recorded video replayed on a screen held in front of the camera
• Real-time deepfake generators that render a synthetic face over a live camera feed
• 3D-printed masks and silicone replicas
• Photo injection attacks that bypass the camera entirely and inject a static image into the video stream

A single-frame analysis cannot distinguish a high-quality deepfake from a real face. The temporal dimension — how a face moves and responds over time — is what separates real from synthetic.

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Interactive liveness analysis

FaceSign uses interactive challenges during the LIVENESS_DETECTION node. Instead of asking for a static selfie, the avatar directs the user through randomized, unpredictable prompts that require real-time physical responses.

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What the system checks

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Why interactive challenges defeat deepfakes

Pre-rendered deepfakes are generated in advance. The attacker produces a synthetic video and replays it during the verification session. This approach fails against interactive challenges because:

1. The challenge is randomized — The avatar selects from a pool of prompts at runtime. The attacker cannot predict what will be asked.
2. The response must be immediate — The system measures the latency between the challenge and the user’s physical response. Real-time deepfake generators introduce measurable processing delay.
3. The response must be physiologically consistent — When a real person turns their head, hundreds of micro-movements occur in facial muscles, skin folds, and lighting angles. Deepfake generators approximate these but introduce statistical artifacts detectable across multiple frames.

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iBeta PAD Level 2

FaceSign’s deepfake detection meets the iBeta Presentation Attack Detection Level 2 standard: PAD Level 2 is the higher of the two iBeta testing levels. It includes advanced attack types (3D masks, video injection) that Level 1 does not test.

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Attack types and detection

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Integration

Deepfake detection runs automatically in two node types:

• LIVENESS_DETECTION — Dedicated interactive liveness check with randomized challenges
• FACE_SCAN — Liveness analysis runs alongside biometric capture

You do not need to configure deepfake detection separately. Adding either node to your verification flow activates the full analysis pipeline.

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Next steps