Enhancing Integrity Verification of Convolutional Neural Network Predictions in a Malicious Model

The widespread deployment of neural networks has raised significant concerns regarding the integrity and privacy of model predictions, especially in malicious environments. Current approaches have explored zero-knowledge proofs for integrity verification. However, they suffer from inefficiency in proving runtime and a lack of rigorous integrity verification for non linear operations. To address these issues, we present a trustwor thy framework for Enhancing Integrity Verification of Convolutional Neural Network predictions (EIV-CNN) in a malicious model, whose key contributions are an efficient optimized sum check protocol and a robust enhanced verification mechanism. Specifically, we first propose an algorithm that enables efficient proving of both batch and collaborative CNN predictions by com bining sumcheck claims of multiple matrix multiplications into one. Moreover, we introduce a non-interactive sumcheck protocol with malicious security (NM-Sumcheck) to serve as a building block for publicly verifying matrix multiplication operations. Furthermore, we introduce a verifiable method for transforming nonlinear operations into matrix operations, enabling their sub sequent evaluation with the NM-Sumcheck protocol. Our EIV CNN provides malicious security, guarantees public verifiability, and preserves model privacy. Empirical results demonstrate that our sumcheck framework achieves constant prover time, verifier time, and proof size. Compared to the state-of-the-art, it achieves up to a 128.56× reduction in prover time, along with significant reductions in communication overhead and enhanced scalability.