Instead of learning today's AI artifact patterns, CerVaLens analyzes immutable physical phenomena embedded in authentic media at the moment of capture — signals that no generator can forge.
CerVaLens takes a fundamentally different approach to deepfake detection. Instead of learning the patterns of today's AI generators — patterns that change with every model update — we analyze immutable physical phenomena embedded in authentic media at the moment of capture.
Electrical Network Frequency (ENF) signals from the power grid leave subtle, random fingerprints in audio and video recordings. These fluctuations are genuine physical phenomena that synthetic generators have no native mechanism to reproduce, because doing so would require replicating the actual electromagnetic environment at a specific time and place.
Camera sensor hardware fingerprints (PRNU — Photo Response Non-Uniformity) provide a second physics-based signal. Every physical camera sensor has a characteristic noise pattern imprinted on every image it captures. AI-generated images lack this hardware signature, making it an orthogonal detection layer that adversarial attacks must trade off against image quality to attempt to remove.
For image authentication, our hybrid architecture additionally combines Discrete Cosine Transform frequency analysis, topological data analysis of structural patterns, and lightweight entropy analysis — creating a multi-layered system effective against Stable Diffusion, DALL-E, Midjourney, FLUX, Adobe Firefly, and emerging models.
Extracts environmental electromagnetic fingerprints from power grid fluctuations embedded in recordings — signatures rooted in real-world electromagnetic conditions, which generators have no native mechanism to reproduce. Forgery requires reproducing the actual electromagnetic environment at a specific location and moment in time.
Identifies the unique Photo Response Non-Uniformity noise pattern of a real camera sensor. AI-generated images lack this physical hardware signature entirely — a detection signal orthogonal to everything adversarial attacks target.
Captures complex structural patterns in the frequency domain using topological data analysis (TDA), identifying subtle manipulation artifacts invisible to conventional methods.
Lightweight spatial entropy analysis across RGB and grayscale channels enables efficient on-device detection with minimal computational overhead.
Multiple independent physics and ML detection layers with decision fusion provide resilient authentication — if one layer is uncertain, others compensate for robust overall accuracy.
CerVaLens' edge-native architecture is not a feature — it is a structural necessity for the defense and high-stakes civilian use cases we serve.
ENF and PRNU signals are rooted in physical reality. No retraining needed as AI generators evolve — our advantage strengthens as statistical detectors fall further behind.
All processing occurs on-device with no cloud dependency. Meets air-gapped, zero-trust, and bandwidth-denied operational requirements.
Optimized for real-time processing on standard Android smartphones via ONNX Runtime. No specialized hardware — runs on devices under $300.
Unified authentication across images, video, and audio through integrated detection algorithms under a single platform.
Substantially lower computational resource requirements than typical cloud-based detection pipelines while maintaining 95–99% detection accuracy across image and video benchmarks (Stability AI image set; LAV-DF video set).
Image analysis runs fully on-device with no data transmission; video and audio use a hybrid mobile-server architecture that keeps the on-device path available in disconnected environments.
Funded competitors — Reality Defender, GetReal Security, Adaptive Security, Sensity AI — operate in the cloud using AI/ML statistical artifact detection. These approaches tend to face generalization challenges across new generators, degrade under social media compression, and depend on network connectivity. CerVaLens is structurally different, not incrementally better.