The world’s first real-time deepfake detector using biological signal analysis.
Intel FakeCatcher represents a paradigm shift in synthetic media detection by moving away from forensic artifact analysis toward biological signal validation. Developed by Intel Labs in collaboration with academic researchers, the platform utilizes remote photoplethysmography (rPPG) to detect subtle blood flow changes in human facial pixels that are currently impossible for generative adversarial networks (GANs) or diffusion models to replicate with biological consistency. As of 2026, the technology is fully optimized for 5th and 6th Gen Intel Xeon Scalable processors, leveraging Intel's Deep Learning Boost and OpenVINO toolkit to achieve millisecond-level inference. Unlike traditional detectors that analyze frame-by-frame inconsistencies, FakeCatcher constructs spatiotemporal maps of blood flow across the face, identifying 'pulses' that confirm human presence. This architectural approach makes it uniquely resilient against evolving deepfake generation techniques. Positioned as an enterprise-grade solution, it is primarily deployed within global news organizations, financial institutions for high-stakes KYC, and social media platforms to maintain information integrity in an era of hyper-realistic synthetic content.
Extracts biological blood flow signals from video pixels by measuring light absorption changes.
The industry-standard open-source benchmark and dataset for identifying AI-generated video manipulation.
The global industry standard for validating synthetic media detection and visual integrity.
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Converts facial signals into 2D maps across time to ensure pulse consistency.
Uses hardware-level acceleration for inferencing 72 concurrent streams on a single server.
Architected to track and analyze up to 12 faces simultaneously in a 4K stream.
Generates visual heatmaps showing where the 'blood flow' is missing or inconsistent.
Leverages AVX-512 VNNI instructions for massive speedups in signal processing.
Validates the heartbeat rhythm over a sequence of 32-64 frames.
Preventing the broadcast of intercepted or manipulated 'man-in-the-middle' video feeds.
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Stopping fraudsters using high-quality digital injection deepfakes during account opening.
Scaling the detection of 'deepfake' misinformation during election cycles.