Towards Robust Model Watermark via Reducing Parametric Vulnerability

Guanhao Gan, Yiming Li, Dongxian Wu, Shu-Tao Xia

Keywords: Model Watermarking, Backdoor Watermark, Ownership Verification, Deep IP Protection

Abstract: Deep neural networks are valuable assets considering their commercial benefits and huge demands for costly annotation and computation resources. To protect the copyright of these deep models, backdoor-based ownership verification becomes popular recently, in which the model owner can watermark the model by embedding a specific behavior before releasing it. The defender (usually the model owner) can identify whether a suspicious third-party model is ``stolen'' from it based on the presence of the behavior. Unfortunately, these watermarks are proven to be vulnerable to removal attacks even like fine-tuning. To further explore this vulnerability, we investigate the parametric space and find there exist many watermark-removed models in the vicinity of the watermarked one, which may be easily used by removal attacks. Inspired by this finding, we propose a minimax formulation to find these watermark-removed models and recover their watermark behavior. Extensive experiments demonstrate that our method improves the robustness of the model watermarking against parametric changes and numerous watermark-removal attacks.

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TL;DR: Based on the observation of the watermarked model in parametric space, we propose a minimax approach to improve the robustness of watermarked models against state-of-the-art removal attacks.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/towards-robust-model-watermark-via-reducing/code)