Zero-Knowledge Proof-Based IP Protection of Visual Large Models of Autonomous Driving
Summary
Visual Large Models (VLMs, AI systems that understand images and are used in self-driving cars) need protection from intellectual property theft, but traditional methods like watermarking hurt their performance. This paper proposes a new protection framework using zero-knowledge proof (a technique that proves something is true without revealing the actual information), which includes a fingerprinting method that improves the ability to detect stolen models without harming the AI's ability to perceive traffic scenes, and a verification protocol called zk-DeepIP that protects both the model and test data from leakage during verification.
Solution / Mitigation
The paper proposes two components: a model fingerprinting method that assigns higher weights to high-discriminability samples near decision boundaries using cross-entropy loss to generate enhanced fingerprints, and the zk-DeepIP protocol, which is an IP verification protocol underpinned by zero-knowledge proof technology that ensures robust security while remaining compatible with existing IP verification methods.
Classification
Related Issues
Original source: http://ieeexplore.ieee.org/document/11622595
First tracked: July 30, 2026 at 08:04 PM
Classified by LLM (prompt v3) · confidence: 85%