Abstract
In recent years, watermarking technology has been widely used as a common information hiding technique in the fields of copyright protection, authentication, and data privacy protection in digital media. However, the development of watermark attack techniques has lagged behind. Improving the efficiency of watermark attack techniques and effectively attacking watermarks has become an urgent problem to be solved. Therefore, this paper proposes a watermark attack network called CAWNet. Firstly, this paper designs a convolution-based watermark attack module (CWABlock), which introduces channel attention mechanism. By replacing fully connected layers with global average pooling layers, the parameter quantity of the network is reduced and the computational efficiency is improved, enabling effective attacks on watermark information. Secondly, in the training phase, we utilize a large-scale real-world image dataset for training and employ data augmentation strategies to enhance the robustness of the network. Finally, we conduct ablation experiments on CWABlock, attention mechanism, and other modules, as well as comparative experiments on different watermark attack methods. The experimental results demonstrate significant improvements in the effectiveness of the proposed watermark attack approach.
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Acknowledgements
This work was funded by Taishan Scholar Program of Shandong (tsqn202306251); Youth Innovation Team of Colleges and Universities in Shandong Province (2022KJ124); National Natural Science Foundation of China (62302249, 62272255, 62302248); The “Chunhui Plan” Cooperative Scientific Research Project of Ministry of Education (HZKY20220482); National Key Research and Development Program of China (2021YFC3340602); Shandong Provincial Natural Science Foundation (ZR2023QF032, ZR2022LZH011, ZR2023QF018, ZR2020MF054); Ability Improvement Project of Science and technology SMES in Shandong Province (2023TSGC0217, 2022TSGC2485); Project of Jinan Research Leader Studio (2020GXRC056); Project of Jinan Introduction of Innovation Team (202228016); Science, Education and Industry Integration Project (2023PY060, 2023PX071, 2023PX006).
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Wang, C., Tian, P., Wei, Z., Li, Q., Xia, Z., Ma, B. (2024). CAWNet: A Channel Attention Watermarking Attack Network Based on CWABlock. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14433. Springer, Singapore. https://doi.org/10.1007/978-981-99-8546-3_4
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DOI: https://doi.org/10.1007/978-981-99-8546-3_4
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