WDNet: Watermark-Decomposition Network for Visible Watermark Removal

• URL: http://arxiv.org/abs/2012.07616v2
• Date: Tue, 15 Dec 2020 03:57:21 GMT
• Title: WDNet: Watermark-Decomposition Network for Visible Watermark Removal
• Authors: Yang Liu, Zhen Zhu, and Xiang Bai
• Abstract summary: The uncertainty of the size, shape, color and transparency of watermarks set a huge barrier for image-to-image translation techniques. We combine traditional watermarked image decomposition into a two-stage generator, called Watermark-Decomposition Network (WDNet) The decomposition formulation enables WDNet to separate watermarks from the images rather than simply removing them.
• Score: 61.14614115654322
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visible watermarks are widely-used in images to protect copyright ownership. Analyzing watermark removal helps to reinforce the anti-attack techniques in an adversarial way. Current removal methods normally leverage image-to-image translation techniques. Nevertheless, the uncertainty of the size, shape, color and transparency of the watermarks set a huge barrier for these methods. To combat this, we combine traditional watermarked image decomposition into a two-stage generator, called Watermark-Decomposition Network (WDNet), where the first stage predicts a rough decomposition from the whole watermarked image and the second stage specifically centers on the watermarked area to refine the removal results. The decomposition formulation enables WDNet to separate watermarks from the images rather than simply removing them. We further show that these separated watermarks can serve as extra nutrients for building a larger training dataset and further improving removal performance. Besides, we construct a large-scale dataset named CLWD, which mainly contains colored watermarks, to fill the vacuum of colored watermark removal dataset. Extensive experiments on the public gray-scale dataset LVW and CLWD consistently show that the proposed WDNet outperforms the state-of-the-art approaches both in accuracy and efficiency. The code and CLWD dataset are publicly available at https://github.com/MRUIL/WDNet.

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