Towards Robust Model Watermark via Reducing Parametric Vulnerability
Guanhao Gan Yiming Li Dongxian Wu Shu-Tao Xia
Deep neural networks are valuable assets considering their commercial
benefits and huge demands for costly annotation and computation resources. To
protect the copyright of DNNs, backdoor-based ownership verification becomes
popular recently, in which the model owner can watermark the model by embedding
a specific backdoor behavior before releasing it. The defenders (usually the
model owners) can identify whether a suspicious third-party model is ``stolen''
from them 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
parameter 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 mini-max 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.
The codes for reproducing our main experiments are available at
\url{https://github.com/GuanhaoGan/robust-model-watermarking}.