Fraud Detection
Deepfakes Can't Fake Biology
AI-generated videos are increasingly indistinguishable from real footage visually. But they fundamentally fail to replicate the subtle biological signals present in authentic human video — physiological patterns that our rPPG technology detects with high accuracy.
Our approach analyzes spatial coherence of blood flow signals across facial regions and temporal consistency over time — signatures that generative models cannot preserve.
Request Demo →Why Biological Signals?
How Deepfake Detection Works
Our pipeline extracts and analyzes biological signals that authentic humans exhibit but synthetic media cannot replicate.
Face & ROI Detection
Computer vision algorithms locate the face and define multiple regions of interest (ROIs) across facial areas.
Biological Signal Extraction
Extract rPPG (remote photoplethysmography) signals from each ROI independently to capture blood flow patterns.
Coherence Analysis
Compute spatial coherence between regions and temporal consistency over time using signal transformations and correlations.
Verdict Output
API returns real or fake classification with confidence score based on physiological signal authenticity.
Platform Capabilities
Enterprise-grade deepfake detection built for scale, security, and seamless integration.
Real-time Detection
Analyze live video streams or recorded content with sub-second response times for interactive verification.
API & SDK Output
REST API returns real/fake classification with confidence scores. Native SDKs available for iOS, Android, and Web.
Biological Signal Analysis
Leverages rPPG-based physiological signals as authenticity markers that generative AI cannot replicate.
No Hardware Required
Works with standard webcams and smartphone cameras. No specialized equipment or sensors needed.
Scalable Architecture
Cloud-native infrastructure handles millions of verifications. On-premise deployment available for sensitive applications.
Enterprise Ready
SOC 2 compliant infrastructure with audit logging, SSO integration, and dedicated support for enterprise deployments.
FakeCatcher: Biological Signal Authentication
Our approach builds on published research demonstrating that biological signals serve as robust authenticity priors for deepfake detection.
“FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals”
Ciftci, Demir, Yin — IEEE TPAMI (arXiv preprint)
Key Research Findings:
- ✓Generative models fail to preserve physiological spatial coherence across face regions
- ✓Temporal consistency of biological signals is absent in synthetic media
- ✓Pipeline: face detection, ROI mapping, signal extraction, coherence analysis
- ✓Signal transformations in time, frequency, and time-frequency domains
- ✓Cross-correlation analysis reveals authenticity signatures
Why Biological Signals?
Unlike visual artifact detection that can be defeated by better generators, biological signal analysis targets fundamental physiological properties that synthetic media cannot replicate.
Read Paper ↗Fraud Detection FAQ
Common questions about deepfake detection and identity verification
How does biological signal analysis detect deepfakes?
Real human faces exhibit subtle physiological signals like blood flow patterns (rPPG) that are spatially coherent across facial regions and temporally consistent over time. AI-generated videos fail to replicate these biological signatures accurately, allowing our system to distinguish authentic from synthetic content.
What types of deepfakes can the system detect?
Our technology detects face-swap deepfakes, face reenactment videos, fully synthetic AI-generated faces, and manipulated video content. The biological signal approach works across different generation methods because it targets fundamental physiological properties rather than visual artifacts.
How is the detection result delivered?
Results are delivered via REST API or native SDK integration. The output includes a real/fake classification, confidence score, and detailed analysis metrics. Integration typically takes 15-30 minutes with our documentation.
What video quality and length is required?
We require a minimum of 10-30 seconds of frontal face video at 720p or higher resolution with adequate lighting. Standard webcam or smartphone video quality is sufficient for accurate detection.
Is the system suitable for real-time video verification?
Yes, our system supports real-time analysis for live video calls and streaming content. Latency is optimized for interactive applications like KYC verification and video call authentication.
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