Deepfake Detection

Faceoff Solves the Problem of Deepfake Videos

Faceoff tackles deepfakes through layered detection and biometric consistency scoring, using a combination of AI cognition and natural inconsistencies in human behavior that current deepfake generation methods cannot replicate.

Deepfake Detection Mechanisms in Faceoff:
Mechanism How It Works
Microexpression Tracking Detects unnatural suppression or repetition of blink/micro-expressions
Lip Sync and Voice Emotion Mismatch Detects desync between speech sentiment and facial emotion
Biometric Drift Monitoring Identifies subtle inconsistencies in eye dilation, pulse rate, and skin tone
Posture-to-Speech Correlation Validates if body posture matches the vocal tone (e.g., aggression vs. passivity)
Multi-AI Ensemble Scoring Uses a dynamic voting system with weighted trust metrics
Why It’s Effective:
  • Deepfakes focus on visuals, but Faceoff challenges behavioral and physiological consistency.
  • Fake speech and visuals can be accurate alone, but rarely align together under scrutiny.
  • Faceoff’s Trust Score, generated from 8 AI engines, highlights inconsistencies that are invisible to the naked eye — or to traditional deepfake detectors.

Faceoff redefines deepfake detection by relying on truth from the body, not just pixels. Its privacy-preserving, cloudless architecture, combined with multimodal AI robustness, positions it as the industry’s most advanced defense against synthetic fraud.

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