A comprehensive survey on robust image watermarking

Documents often exhibit various forms of degradation, which make it hard to be read and substantially deteriorate the performance of an OCR system. In this paper, we propose an effective end-to-end framework named document enhancement generative adversarial networks (DE-GAN) that uses the conditional GANs (cGANs) to restore severely degraded document images. To the best of our knowledge, this practice has not been studied within the context of generative adversarial deep networks. We demonstrate that, in different tasks (document clean up, binarization, deblurring and watermark removal), DE-GAN can produce an enhanced version of the degraded document with a high quality. In addition, our approach provides consistent improvements compared to state-of-the-art methods over the widely used DIBCO 2013, DIBCO 2017, and H-DIBCO 2018 datasets, proving its ability to restore a degraded document image to its ideal condition. The obtained results on a wide variety of degradation reveal the flexibility of the proposed model to be exploited in other document enhancement problems. 

/pdf/de-gan-a-conditional-generative-adversarial-network-for-3re9eaa0.pdf

DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement

Digital watermark is a commonly used technique to protect the copyright of medias. Simultaneously, to increase the robustness of watermark, attacking technique, such as watermark removal, also gets the attention from the community. Previous watermark removal methods require to gain the watermark location from users or train a multi-task network to recover the background indiscriminately. However, when jointly learning, the network performs better on watermark detection than recovering the texture. Inspired by this observation and to erase the visible watermarks blindly, we propose a novel two-stage framework with a stacked attention-guided ResUNets to simulate the process of detection, removal and refinement. In the first stage, we design a multi-task network called SplitNet. It learns the basis features for three sub-tasks altogether while the task-specific features separately use multiple channel attentions. Then, with the predicted mask and coarser restored image, we design RefineNet to smooth the watermarked region with a mask-guided spatial attention. Besides network structure, the proposed algorithm also combines multiple perceptual losses for better quality both visually and numerically. 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. The code is available at this http URL.

Split then Refine: Stacked Attention-guided ResUNets for Blind Single Image Visible Watermark Removal.

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 water-mark 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 dataset CLWD is publicly available at https://github.com/ MRUIL/WDNet.

/pdf/wdnet-watermark-decomposition-network-for-visible-watermark-4h63bcqmx9.pdf

WDNet: Watermark-Decomposition Network for Visible Watermark Removal

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 this https URL.

pdf/wdnet-watermark-decomposition-network-for-visible-watermark-32ckohldp4.pdf

WDNet: Watermark-Decomposition Network for Visible Watermark Removal.

Visible watermark is extensively used for copyright protection with the wide spread of online image. To verify its effectiveness, there are many researches attempt to detect and remove visible watermark thus it increasingly becomes a hot research topic. Most of the existing methods require to obtain the prior knowledge from watermark, which is not applicable for images with unknown and diverse watermark patterns. Therefore, developing a data-driven algorithm that suits for various watermarks is more significant in realistic application. To address the challenging visible watermark task, we propose the first general deep learning based framework, which can precisely detect and remove a variety of watermark with convolutional networks. Specifically, general object detection methods are adopted for watermark detection and watermark removal is implemented by using image-to-image translation model. Comprehensive empirical evaluation are conducted on a new large-scale dataset, which consists of 60000 watermarked images with 80 watermark classes, the experimental results demonstrate the feasible of our introduced framework in practical. This research aims to increase copyright awareness for the spread of online images. A reminder of this paper is that visible watermark should be designed to not only be striking enough for ownership declaration, but to be more resistant for removal attacking.

Large-Scale Visible Watermark Detection and Removal with Deep Convolutional Networks

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.

Towards Photo-Realistic Visible Watermark Removal with Conditional Generative Adversarial Networks.

Towards Photo-Realistic Visible Watermark Removal with Conditional Generative Adversarial Networks

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Large-Scale Visible Watermark Detection and Removal with Deep Convolutional Networks

Towards Photo-Realistic Visible Watermark Removal with Conditional Generative Adversarial Networks.

Towards Photo-Realistic Visible Watermark Removal with Conditional Generative Adversarial Networks