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Deep Neural Networks (DNN) are gaining higher commercial values in computervision applications, e.g., image classification, video analytics, etc. Thiscalls for urgent demands of the intellectual property (IP) protection of DNNmodels. In this paper, we present a novel watermarking scheme to achieve theownership verification of DNN architectures. Existing works all embeddedwatermarks into the model parameters while treating the architecture as publicproperty. These solutions were proven to be vulnerable by an adversary todetect or remove the watermarks. In contrast, we claim the model architecturesas an important IP for model owners, and propose to implant watermarks into thearchitectures. We design new algorithms based on Neural Architecture Search(NAS) to generate watermarked architectures, which are unique enough torepresent the ownership, while maintaining high model usability. Suchwatermarks can be extracted via side-channel-based model extraction techniqueswith high fidelity. We conduct comprehensive experiments on watermarked CNNmodels for image classification tasks and the experimental results show ourscheme has negligible impact on the model performance, and exhibits strongrobustness against various model transformations and adaptive attacks.

Ownership Verification of DNN Architectures via Hardware Cache Side Channels