Digital watermarking has been widely used to protect the copyright and integrity of multimedia data. Previous studies mainly focus on designing watermarking techniques that are robust to attacks of destroying the embedded watermarks. However, the emerging deep learning based image generation technology raises new open issues that whether it is possible to generate fake watermarked images for circumvention. In this paper, we make the first attempt to develop digital image watermark fakers by using generative adversarial learning. Suppose that a set of paired images of original and watermarked images generated by the targeted watermarker are available, we use them to train a watermark faker with U-Net as the backbone, whose input is an original image, and after a domain-specific preprocessing, it outputs a fake watermarked image. Our experiments show that the proposed watermark faker can effectively crack digital image watermarkers in both spatial and frequency domains, suggesting the risk of such forgery attacks.
翻译:数字水标记已被广泛用于保护多媒体数据的版权和完整性。以前的研究主要侧重于设计对破坏嵌入水印标志的攻击具有强大威力的水标记技术。然而,正在形成的深层次的基于学习的图像生成技术提出了新的开放问题,即是否有可能生成假的水标记图像以规避。在本文中,我们第一次尝试通过使用基因化对抗性学习来开发数字图像水标记假体。假设有一组由目标水标记员生成的原始和水标记图像的配对图像,我们用它们来培训一个以U-Net作为主干线的水标记假图案,其输入为原始图像,并在一个特定域的预处理后产生假的水标记图案。我们的实验显示,拟议的水标记假像可以在空间和频率领域有效地破解数字水标记水标记,表明这种伪造攻击的风险。