Introduction
PrivateID delivers passive, privacy-preserving facial liveness detection that operates entirely on the edge—no images, biometric templates, or PII are ever transmitted or stored. This on-device approach eliminates organizational risks associated with data breaches and global privacy law obligations while ensuring real-time performance that outpaces cloud-based tools and remains resilient even in low-bandwidth environments.
Edge-based liveness detection prevents spoofing attempts, including printed and digital presentation attacks, 3D masks, and deepfakes. PrivateID’s models have been independently validated through successful completion of iBeta Presentation Attack Detection (PAD) Level 1 and Level 2 assessments. Additional on-device integrity checks—such as detecting jailbroken phones or verifying camera authenticity—further enhance security and inform policy locally without requiring server calls.
1. Edge‑First Processing
PrivateID’s edgeAI technology performs on-device camera capture, image quality checks, facial landmark detection, facial liveness and anti-spoof detection, selfie-to-photo ID matching directly on-device without transmitting any images or personally identifiable information (PII). It operates seamlessly across general-purpose devices, including smartphones, desktops, point-of-sale (POS) systems, kiosks, and other camera-enabled hardware.
By shifting these processes to the edge, PrivateID enhances user privacy while reducing operational risks and costs for organizations with a fast and seamless user experience.
2. Single‑Pass Decisions — No Retries or Reattempts
Real-time user feedback ensures that the user completes the passive capture experience quickly on the first attempt, delivering clear and consistent outcomes. No retries are needed, eliminating user frustration and streamlining the experience. This one-and-done approach also prevents system probing through selective reattempts and simplifies operations by making each attempt a single, high-confidence decision that is easy to measure and audit.
3. Performance & Reliability
PrivateID’s facial liveness detection solution has been tested rigorously by customers and third parties including iBeta, successfully achieving PAD 1 and PAD 2 certifications. This provides our customers with the highest levels of spoof detection, even against the most advanced generative AI tools.
As the liveness check occurs on‑device, results are delivered in real time with consistently low latency. Quality gates run locally to evaluate framing, glare, motion blur, and lighting so that outcomes remain robust without falling back to retry loops.
4. Privacy & Compliance by Design
PrivateID’s liveness detection runs at the edge delivering unmatched privacy by performing all processing directly on the user’s device—no images, biometrics, or personal data are ever transmitted or stored. This on-device approach eliminates the risk of data breaches and removes global privacy compliance burdens for businesses.
5. PAD Techniques & Integrity Signals
PrivateID’s system prioritizes passive liveness, analyzing texture, reflectance, optical flow, depth cues, rippling and aliasing patterns, and micro‑motions to detect print, video replay, and screen‑based attacks without user challenges. Local device and session integrity signals, including jailbreak or root detection and camera authenticity metadata, inform policy while keeping all processing on the device.
6. Policy‑Based Outcomes
PrivateID returns outcomes that match business rules and risk posture. Deployments can use binary “Live/Not Live” decisions or apply a policy‑thresholded confidence band. Only the decision returns to back‑end services; raw media and derived biometrics remain on the device unless they need to be transmitted for regulatory or legal reasons.
7. Interoperability & Deployment
PrivateID offers modular SDKs for Swift (iOS), Kotlin (Android), web (WASM) and a simple API service that works across general purpose hardware including desktop, mobile, POS, kiosks, and more. Integration is seamless with customizable UI components and low‑code flows. PrivateID offers multiple hosting options including SaaS, PaaS, or on‑prem, allowing flexibility and control for customers.
8. Brand Control & White Labeling
Enterprises can match the experience to their brand with full control over copy, styling, animations and policy messaging. Teams own the entire user journey, including local privacy notices and consent text when required, so the flow feels native and trustworthy.
9. User Experience
PrivateID provides an on‑device assistant that gives real‑time guidance on camera angle, distance, glare, and lighting, helping people complete the check on the first try—without retries. Interactions finish in milliseconds and feel private and respectful because data never leaves the device.
10. Use Cases – Proof of Human
PrivateID enhances user enrollment with highly accurate, on-device Presentation Attack Detection (PAD), ensuring authenticity without transmitting images to a server. It supports continuous authentication through edge-based liveness checks for both 1:1 and 1:N facial biometric matching—enabling secure, passwordless login, transaction authorization, account recovery, and identity verification for agentic AI applications. It can detect spoofing in both still images and video streams, enabling proof of human detection in streamlining content and conferencing platforms.
Retailers, venues, and other offline or low-connectivity environments benefit from PrivateID’s reliable, privacy-preserving liveness detection at self-checkout stations, kiosks, and stadium entry points, ensuring trust and security anywhere users interact.
Summary
PrivateID Liveness Detection is an edge‑first, privacy‑preserving PAD solution that delivers real‑time, single‑pass decisions—no retries required while maintaining user privacy. By keeping all processing on‑device, it outpaces cloud‑based alternatives while minimizing regulatory exposure. With modular SDKs, white‑label UX, and policy‑driven outcomes, organizations can harden verification and authentication at scale in both still image and video streaming without compromising speed, privacy, or user experience.