Proceedings

Fine-tuning Is Not Enough: A Simple yet Effective Watermark Removal Attack for DNN Models

Shangwei Guo, Tianwei Zhang, Han Qiu, Yi Zeng, Tao Xiang, Yang Liu

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 3635-3641.

https://doi.org/10.24963/ijcai.2021/500

Keywords

  • Multidisciplinary Topics and Applications: Economic and Finance
  • Multidisciplinary Topics and Applications: Security and Privacy

Abstract

Watermarking has become the tendency in protecting the intellectual property of DNN models. Recent works, from the adversary's perspective, attempted to subvert watermarking mechanisms by designing watermark removal attacks. However, these attacks mainly adopted sophisticated fine-tuning techniques, which have certain fatal drawbacks or unrealistic assumptions. In this paper, we propose a novel watermark removal attack from a different perspective. Instead of just fine-tuning the watermarked models, we design a simple yet powerful transformation algorithm by combining imperceptible pattern embedding and spatial-level transformations, which can effectively and blindly destroy the memorization of watermarked models to the watermark samples. We also introduce a lightweight fine-tuning strategy to preserve the model performance. Our solution requires much less resource or knowledge about the watermarking scheme than prior works. Extensive experimental results indicate that our attack can bypass state-of-the-art watermarking solutions with very high success rates. Based on our attack, we propose watermark augmentation techniques to enhance the robustness of existing watermarks.

Shangwei Guo, Tianwei Zhang, Han Qiu, Yi Zeng, Tao Xiang, and Yang Liu. 2021. Fine-tuning Is Not Enough: A Simple yet Effective Watermark Removal Attack for DNN Models. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 3635–3641. https://doi.org/10.24963/ijcai.2021/500
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