Decentralized Trust for AI: Verifying Proprietary DNN Inference with Blockchain, zk-SNARKs, and zk-STARKs

Abstract

As artificial intelligence becomes deeply embedded in critical sectors like finance and medicine, we face a pressing challenge: how to guarantee its integrity. At the heart of this issue is a conflict between the proprietary nature of AI models, which are valuable assets, and the growing need for transparency in their operations. This paper lays out an architectural blueprint that resolves this tension by bringing together blockchain technology and Zero-Knowledge Proofs (ZKPs). We show how it's possible to verifiably confirm that an AI model has run correctly without exposing any of its confidential internal parameters. We walk through a simulation where a Deep Neural Network (DNN) produces an inference, and a ZKP is generated to prove the calculation used the legitimate model weights. This proof, along with the public data, is then recorded on a decentralized ledger, creating a permanent, auditable trail. A key part of our work is a comparison of two major ZKP technologies, zk-SNARKs and zk-STARKs, where we break down their respective trade-offs. Our simulation's effectiveness is demonstrated through resilience testing; it successfully identified and rejected 100% of fraudulent attempts, including both tampered outputs and counterfeit models. This demonstrates the architecture's efficiency in creating a provably secure and auditable trail, lighting a path toward genuinely trustworthy AI.