  Visible watermark plays an important role in image copyright protection and
the robustness of a visible watermark to an attack is shown to be essential. To
evaluate and improve the effectiveness of watermark, watermark removal attracts
increasing attention and becomes a hot research top. Current methods cast the
watermark removal as an image-to-image translation problem where the
encode-decode architectures with pixel-wise loss are adopted to transfer the
transparent watermarked pixels into unmarked pixels. However, when a number of
realistic images are presented, the watermarks are more likely to be unknown
and diverse (i.e., the watermarks might be opaque or semi-transparent; the
category and pattern of watermarks are unknown). When applying existing methods
to the real-world scenarios, they mostly can not satisfactorily reconstruct the
hidden information obscured under the complex and various watermarks (i.e., the
residual watermark traces remain and the reconstructed images lack reality). To
address this difficulty, in this paper, we present a new watermark processing
framework using the conditional generative adversarial networks (cGANs) for
visible watermark removal in the real-world application. The proposed method
enables the watermark removal solution to be more closed to the photo-realistic
reconstruction using a patch-based discriminator conditioned on the watermarked
images, which is adversarially trained to differentiate the difference between
the recovered images and original watermark-free images. Extensive experimental
results on a large-scale visible watermark dataset demonstrate the
effectiveness of the proposed method and clearly indicate that our proposed
approach can produce more photo-realistic and convincing results compared with
the state-of-the-art methods.
