Zero-Knowledge Proof-Based IP Protection of Visual Large Models of Autonomous Driving

Summary

This paper proposes zk-DeepIP, an intellectual property protection framework for Visual Large Models used in autonomous driving, built on zero-knowledge proof technology. Its model fingerprinting method adds a prior-knowledge-free sample discrimination module that weights samples near decision boundaries, which the authors report raises the average AUC by 0.03 on CIFAR-10 across attacks, datasets and models, and by 0.12 in transfer learning scenarios on CIFAR-100 and CIFAR10-c. The verification protocol keeps both perception models and their test cases private during IP checks.

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