Faceoff’s deepfake detection combines facial analysis, motion tracking, and texture anomalies. By assessing blinking, symmetry, and lighting with temporal coherence, it uncovers synthetic edits and ensures video authenticity and trust.
Multi-AI Fusion Architecture
Faceoff is not reliant on a single modality; it fuses 8 AI models working in parallel (vision, audio, physiological signal estimation) for a more holistic trust and authenticity assessment.
→ Why it’s better: Traditional systems mostly use only facial recognition or emotion detection alone. Faceoff combines cross-domain cues for robustness.
Privacy-Preserving API Deployment
Faceoff does not ask for or store video data. It only provides stateless APIs for enterprises to use on their private cloud or infrastructure. Only metadata like number of API calls is tracked.
→ Why it’s better: Most SaaS tools process videos in vendor’s cloud, risking data breaches. Faceoff's architecture supports on-premise privacy.
How Each of the 8 AI Models Work
| Model Name | Input | What It Detects |
|---|---|---|
| Facial Emotion Recognition | Video frames | Human emotion state (anger, joy, etc.) |
| Eye-Tracking Emotion Detection | Eye region | Stress, deception cues, fatigue |
| Posture Analysis | Full body from video | Nervous gestures, alertness, assertiveness |
| Heart Rate Estimation | Face color changes | Real-time BPM |
| Oxygen Saturation Detection | Facial RGB video | Blood oxygen (SpO₂) estimate |
| Speech Sentiment Analysis | Voice waveform | Emotion from spoken content |
| Audio Tone Sentiment | Audio pitch + tone | Tone-based intent (anger, sarcasm) |
| Deepfake Detection | Frame consistency | Video authenticity probability |
30-Second Rapid Video Analysis
All models are trained and optimized for short clips (15–30 seconds), making them suitable for social media, HR screening, fraud detection, and forensic analysis.
→ Why it’s better: Other systems need longer footage or higher resolution to work reliably.
Physiological Signal Estimation (SpO₂, HR) from Face Only
Extracting remote PPG and oxygen saturation using camera input — no wearables or special sensors.
→ Why it’s better: Industry tools don’t integrate bio-signals into authenticity assessment, making Faceoff uniquely biomimetic.
Behavioral and Sentiment Correlation Model
Maps emotion, posture, voice tone, and speech semantics to validate behavioral consistency.
→ Why it’s better: Deepfakes may mimic expressions but struggle to maintain alignment across modalities.
Real-Time AI Decision Pipeline
Faceoff offers real-time feedback (2–3 seconds) on video input.
→ Why it’s better: Traditional forensic or ML pipelines may take minutes or hours for analysis.