rPPG-Based Deepfake Detection and Liveness Verification
Deepfakes can replicate a face, but they cannot replicate a heartbeat. Circadify reads the subtle blood flow signatures beneath human skin using remote photoplethysmography — the one biometric signal that synthetic media cannot forge. If there is no real pulse, there is no real person.
Why Choose Us
Advanced rPPG technology that transforms any camera into a health monitoring device
Blood Flow Detection via Camera
Circadify analyzes standard video feeds to detect the micro-fluctuations in skin color caused by real blood pulsing beneath the surface. Deepfakes and synthetic faces lack these hemodynamic signatures entirely, making physiological analysis a definitive liveness signal.
Presentation Attack Defense
Printed photos, screen replays, 3D masks, and AI-generated face swaps all share one fatal flaw: no living circulatory system. Circadify identifies presentation attacks by verifying the presence of genuine cardiovascular signals that no spoofing method can reproduce.
Synthetic Media Identification
Generative AI produces faces that fool human eyes and traditional facial recognition systems. Circadify goes deeper — analyzing the temporal blood volume pulse patterns that only exist in footage of real, living humans. Synthetic media fails this biological test every time.
Passive Liveness in Seconds
No blinking prompts. No head-turning instructions. No challenge-response friction. Circadify performs liveness verification passively from existing video — users simply face the camera naturally while blood flow analysis runs silently in the background.
Video Call Verification
Verify that the person on a live video call is physically present and not a deepfake injection. Circadify analyzes real-time video streams to confirm genuine hemodynamic activity, protecting remote onboarding, teleconferencing, and video-based identity proofing.
Standard Camera Integration
No specialized hardware, infrared sensors, or depth cameras required. Circadify works with the RGB cameras already embedded in smartphones, laptops, and kiosks — deploying deepfake detection across your existing infrastructure without hardware upgrades.
Stop Deepfakes Before They Breach Your Pipeline
Add physiological liveness detection to your identity verification stack.
Explore Fraud Detection SolutionsTrusted Across Financial Services and Identity Security
How rPPG-based liveness detection protects against fraud at every identity verification touchpoint
KYC and Identity Onboarding
Identity verification vendors integrate Circadify to add a physiological liveness layer that synthetic identities cannot defeat. During selfie-based KYC flows, rPPG confirms the applicant is a living person with real blood flow — catching deepfake injection attacks that document-only checks miss.
Biological proof of lifeBanking Transaction Authentication
Banks and fintech platforms use Circadify to verify that high-value transactions are authorized by a physically present account holder. Blood flow analysis during face-based authentication stops fraudsters using AI-generated face swaps to impersonate customers and bypass biometric checks.
Transaction-level fraud defenseRemote Video Verification
For video-based identity proofing and remote onboarding calls, Circadify analyzes the live feed to confirm the participant is a real human — not a deepfake streamed through virtual camera software. Hemodynamic signals provide continuous liveness assurance throughout the session.
Real-time deepfake screeningContactless vitals technology for healthcare, insurance, and security
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Ready to Make Deepfakes Irrelevant?
Every identity verification pipeline has a deepfake vulnerability. Circadify closes it with the one signal synthetic media cannot fake — real blood flow. Our rPPG-based liveness detection integrates into existing KYC workflows, banking authentication, and video verification systems to provide physiological proof that the person on camera is alive and present. Request a demo to see how blood flow analysis catches what traditional biometrics miss.