  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.
