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

Cheng, Danni; Li, Xiang; Li, Weihong; Lu, Chan; Li, Fake; Zhao, Hua; Zheng, Wei‐Shi

doi:10.1007/978-3-030-03338-5_3

Cited by 28 publications

(51 citation statements)

References 15 publications

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“…G(x) denotes the output of generators and y denote the ground truth watermark-free image. Apart from the l 1 loss, it is beneficial to inject the perceptual loss for watermark removal [2]. The perceptual loss function of our network can be expressed as:…”

Section: Objective Functionmentioning

confidence: 99%

“…In LVW dataset, around 80% sorts of watermark are used for training, and the remaining 20% are for test (see Fig. 3), which is the same as [2] . This setting meets the requirements of unknown watermarks removal in real-world scenarios well.…”

Section: Datasets and Settingsmentioning

confidence: 99%

“…This setting meets the requirements of unknown watermarks removal in real-world scenarios well. Both of the Peak signal to noise ratio (PSNR) and structural dissimilarity image index (DSSIM), measuring the similarity between the recovered image and the ground truth one, are adopted as evaluation metrics by previous work (e.g., [3,2]). However, both metrics fail to capture and accurately assess image quality with respect to the human visual system [9].…”

Section: Datasets and Settingsmentioning

confidence: 99%

“…Nowadays, people tend to post photos and videos on the Internet for sharing and preserving memories of events and so on. To protect the copyright of photos and videos, [2] recovered results which remain a few residual watermark traces and are perceptually not photo-realistic (b), our framework is able to generate more convincing results (c).…”

Section: Introductionmentioning

confidence: 99%

“…Here, the patch-based discriminator network is conditioned on the input watermarked images and is trained to differentiate the difference between the recovered images and original watermark-free images. Additionally, we use a content loss motivated by perceptual similarity and pixel similarity [7,2], which consists of the L1 loss and the perceptual loss. With both the adversarial loss and the content loss, our framework is able to generate more convincing recovered results from images marked by diverse unknown watermarks ( Fig.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Cheng

et al. 2019

Lecture Notes in Computer Science

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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.

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Section: Objective Functionmentioning

confidence: 99%

Section: Datasets and Settingsmentioning

confidence: 99%