VisionGuard: Cost-Sensitive AI Attestation with Quorum-Verified Blockchain Enforcement

Web3 platforms face a critical challenge: once unsafe content is minted on-chain, it becomes immutable and irrevocable. Traditional NSFW classifiers operate off-chain without cryptographic guarantees, leaving blockchain ecosystems vulnerable to harmful content. We present VisionGuard, a unified moderation framework that integrates cost-sensitive AI decision-making with blockchain-based enforcement. Our system combines calibrated NSFW classification, abstention-based triage for uncertain cases, perceptual hashing for near-duplicate detection, and on-chain k-of-n quorum attestation using EIP-712 signatures. We establish formal guarantees for: (i) Bayes-optimal cost-sensitive thresholds minimizing asymmetric error costs, (ii) optimal abstention intervals for human review, (iii) monotone false-negative reduction under classifier-pHash fusion, (iv) quorum compromise bounds, and (v) end-to-end unsafe-mint probability. Empirical validation on a zero-shot NSFW task demonstrates 82% accuracy (AUC =0.88), with the Bayes-optimal threshold (τ∗=0.1) reducing expected cost to 27,520 versus 54,942 at the F1-optimal threshold—a 50% improvement. Calibrated abstention further lowers harm (cost =10,649.5), while a 3-of-5 quorum with oracle compromise p=0.1 yields break probability Pbreak