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Watermarking has become a plausible candidate for ownership verification andintellectual property protection of deep neural networks. Regarding imageclassification neural networks, current watermarking schemes uniformly resortto backdoor triggers. However, injecting a backdoor into a neural networkrequires knowledge of the training dataset, which is usually unavailable in thereal-world commercialization. Meanwhile, established watermarking schemesoversight the potential damage of exposed evidence during ownershipverification and the watermarking algorithms themselves. Those concerns declinecurrent watermarking schemes from industrial applications. To confront thesechallenges, we propose a knowledge-free black-box watermarking scheme for imageclassification neural networks. The image generator obtained from a data-freedistillation process is leveraged to stabilize the network's performance duringthe backdoor injection. A delicate encoding and verification protocol isdesigned to ensure the scheme's security against knowledgable adversaries. Wealso give a pioneering analysis of the capacity of the watermarking scheme.Experiment results proved the functionality-preserving capability and securityof the proposed watermarking scheme.

Knowledge-Free Black-Box Watermark and Ownership Proof for Image Classification Neural Networks