Multi-bit watermarking (MW) has been developed to improve robustness against signal processing operations and geometric distortions. To this end, benchmark tools that test robustness by applying simulated attacks on watermarked images are available. However, limitations in these general attacks exist since they cannot exploit specific characteristics of the targeted MW. In addition, these attacks are usually devised without consideration of visual quality, which rarely occurs in the real world. To address these limitations, we propose a watermarking attack network (WAN), a fully trainable watermarking benchmark tool that utilizes the weak points of the target MW and induces an inversion of the watermark bit, thereby considerably reducing the watermark extractability. To hinder the extraction of hidden information while ensuring high visual quality, we utilize a residual dense blocks-based architecture specialized in local and global feature learning. A novel watermarking attack loss is introduced to break the MW systems. We empirically demonstrate that the WAN can successfully fool various block-based MW systems. Moreover, we show that existing MW methods can be improved with the help of the WAN as an add-on module.
翻译:多位水标记(MW)是为提高抗信号处理操作和几何扭曲的稳健性而开发的。为此目的,有基准工具,通过对水标记图像进行模拟攻击来测试稳健性。然而,这些一般性攻击由于无法利用目标兆瓦的具体特性而存在局限性。此外,这些攻击通常是在没有考虑到视觉质量的情况下设计的,在现实世界中很少发生。为了解决这些局限性,我们提议建立一个水标记攻击网络(WAN),这是一个完全可训练的水标记基准工具,利用目标兆瓦的薄弱点,引出水标记位的反转,从而大大降低水标记提取能力。为了在确保高视觉质量的同时阻碍提取隐藏信息,我们使用了本地和全球特征学习中专门设计的残余密集区块结构。引入了一种新的水标记攻击损失来破坏兆瓦系统。我们从经验上证明,广域网能够成功地愚弄各种块基兆瓦系统。此外,我们表明,现有的兆瓦方法可以通过广作为附加模块的帮助加以改进。