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.

Related papers

• A Baseline Method for Removing Invisible Image Watermarks using Deep Image Prior [2.5096001124754728]
  We present a black-box method for removing invisible image watermarks without any dataset of watermarked images or knowledge about the watermark system. We show that from the intermediate steps of DIP one can reliably find an evasion image that can remove invisible watermarks.
  arXiv  Detail & Related papers  (2025-02-19T07:30:19Z)
• Invisible Watermarks: Attacks and Robustness [0.3495246564946556]
  We introduce novel improvements to watermarking robustness and minimize degradation on image quality during attack. We propose a custom watermark remover network which preserves one of the watermarking modalities while completely removing the other during decoding. Our evaluation suggests that 1) implementing the watermark remover model to preserve one of the watermark modalities when decoding the other modality slightly improves on the baseline performance, and that 2) LBA degrades the image significantly less compared to uniform blurring of the entire image.
  arXiv  Detail & Related papers  (2024-12-17T03:50:13Z)
• ESpeW: Robust Copyright Protection for LLM-based EaaS via Embedding-Specific Watermark [50.08021440235581]
  Embeds as a Service (Eding) is emerging as a crucial role in AI applications. Eding is vulnerable to model extraction attacks, highlighting the urgent need for copyright protection. We propose a novel embedding-specific watermarking (ESpeW) mechanism to offer robust copyright protection for Eding.
  arXiv  Detail & Related papers  (2024-10-23T04:34:49Z)
• A self-supervised CNN for image watermark removal [102.94929746450902]
  We propose a self-supervised convolutional neural network (CNN) in image watermark removal (SWCNN) SWCNN uses a self-supervised way to construct reference watermarked images rather than given paired training samples, according to watermark distribution. Taking into account texture information, a mixed loss is exploited to improve visual effects of image watermark removal.
  arXiv  Detail & Related papers  (2024-03-09T05:59:48Z)
• DeepEclipse: How to Break White-Box DNN-Watermarking Schemes [60.472676088146436]
  We present obfuscation techniques that significantly differ from the existing white-box watermarking removal schemes. DeepEclipse can evade watermark detection without prior knowledge of the underlying watermarking scheme. Our evaluation reveals that DeepEclipse excels in breaking multiple white-box watermarking schemes.
  arXiv  Detail & Related papers  (2024-03-06T10:24:47Z)
• Removing Interference and Recovering Content Imaginatively for Visible Watermark Removal [63.576748565274706]
  This study introduces the Removing Interference and Recovering Content Imaginatively (RIRCI) framework. RIRCI embodies a two-stage approach: the initial phase centers on discerning and segregating the watermark component, while the subsequent phase focuses on background content restoration. To achieve meticulous background restoration, our proposed model employs a dual-path network capable of fully exploring the intrinsic background information beneath semi-transparent watermarks.
  arXiv  Detail & Related papers  (2023-12-22T02:19:23Z)
• Watermark Faker: Towards Forgery of Digital Image Watermarking [10.14145437847397]
  We make the first attempt to develop digital image watermark fakers by using generative adversarial learning. Our experiments show that the proposed watermark faker can effectively crack digital image watermarkers in both spatial and frequency domains.
  arXiv  Detail & Related papers  (2021-03-23T12:28:00Z)
• Split then Refine: Stacked Attention-guided ResUNets for Blind Single Image Visible Watermark Removal [69.92767260794628]
  Previous watermark removal methods require to gain the watermark location from users or train a multi-task network to recover the background indiscriminately. We propose a novel two-stage framework with a stacked attention-guided ResUNets to simulate the process of detection, removal and refinement. We extensively evaluate our algorithm over four different datasets under various settings and the experiments show that our approach outperforms other state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2020-12-13T09:05:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.