Autonomous Media Verification
Vision
Frame-accurate proofs. Zero perceptual hallucination.
A high-throughput distributed pipeline for real-time deepfake detection and media provenance validation, computing deterministic perceptual hashing and cryptographic provenance chains across video streams at sub-50ms latency.
Real-time perceptual video hashing
Vision ingests live video streams, extracts keyframes at 60 fps, and computes spatial-temporal perceptual hashes using CUDA-accelerated C++ kernels. Hashes remain robust against compression, bitrate throttling, and re-encoding while catching micro-anomalies.
Deepfake anomaly detection pipeline
An ensemble of compact spatial vision transformers analyzes subtle facial boundary artifacts and temporal inconsistencies across frame sequences. Inferences run within a 12ms GPU budget per frame.
Cryptographic media provenance ledger
Validated media chunks are hashed into a verifiable Merkle tree and stamped with cryptographic origin certificates, giving downstream platforms mathematical certainty about source capture authenticity.
Low-latency distributed worker swarm
Built as an asynchronous Go worker swarm coordinated via NATS JetStream, Vision automatically balances video segment processing across heterogeneous worker nodes with zero dropped frames.