Operational computer vision pipelines combine training data from multiple contributors, pre-trained or vendor-supplied models, and inference outputs consumed by mission-critical downstream systems. This creates distinct integrity and assurance risks across the data, model, and inference lifecycle, including deliberately mislabelled samples, backdoor trojans, unauthorized model substitution, and unverified inference results.
Build a model-agnostic assurance system that continuously assesses and guarantees the integrity of computer-vision models, datasets, and inference records in multi-contributor defense environments.
Detecting latent trigger-based backdoors in black-box vision models without hurting nominal accuracy, cryptographically binding inference traces to model weights, and ensuring verifiable data provenance without crippling pipeline throughput.
A unified integrity platform featuring cryptographic provenance tracking (Merkle DAGs / verifiable attestations), automated data sanitation and backdoor scanning (clean-label attack detection, Neural Cleanse trigger inversion), model fingerprinting, and tamper-proof inference verification.