Visual Watermark Removal Based on Deep Learning

• URL: http://arxiv.org/abs/2302.11338v1
• Date: Tue, 7 Feb 2023 04:18:47 GMT
• Title: Visual Watermark Removal Based on Deep Learning
• Authors: Rongfeng Wei
• Abstract summary: We propose a deep learning method based technique for visual watermark removal. Inspired by the strong image translation performance of the U-structure, an end-to-end deep neural network model named AdvancedUnet is proposed to extract and remove the visual watermark simultaneously.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years as the internet age continues to grow, sharing images on social media has become a common occurrence. In certain cases, watermarks are used as protection for the ownership of the image, however, in more cases, one may wish to remove these watermark images to get the original image without obscuring. In this work, we proposed a deep learning method based technique for visual watermark removal. Inspired by the strong image translation performance of the U-structure, an end-to-end deep neural network model named AdvancedUnet is proposed to extract and remove the visual watermark simultaneously. On the other hand, we embed some effective RSU module instead of the common residual block used in UNet, which increases the depth of the whole architecture without significantly increasing the computational cost. The deep-supervised hybrid loss guides the network to learn the transformation between the input image and the ground truth in a multi-scale and three-level hierarchy. Comparison experiments demonstrate the effectiveness of our method.

Related papers

• 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)
• Perceptive self-supervised learning network for noisy image watermark removal [59.440951785128995]
  We propose a perceptive self-supervised learning network for noisy image watermark removal (PSLNet) Our proposed method is very effective in comparison with popular convolutional neural networks (CNNs) for noisy image watermark removal.
  arXiv  Detail & Related papers  (2024-03-04T16:59:43Z)
• WMFormer++: Nested Transformer for Visible Watermark Removal via Implict Joint Learning [68.00975867932331]
  Existing watermark removal methods mainly rely on UNet with task-specific decoder branches. We introduce an implicit joint learning paradigm to holistically integrate information from both branches. The results demonstrate our approach's remarkable superiority, surpassing existing state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2023-08-20T07:56:34Z)
• Exploring Structure Consistency for Deep Model Watermarking [122.38456787761497]
  The intellectual property (IP) of Deep neural networks (DNNs) can be easily stolen'' by surrogate model attack. We propose a new watermarking methodology, namely structure consistency'', based on which a new deep structure-aligned model watermarking algorithm is designed.
  arXiv  Detail & Related papers  (2021-08-05T04:27:15Z)
• Robust Watermarking using Diffusion of Logo into Autoencoder Feature Maps [10.072876983072113]
  In this paper, we propose to use an end-to-end network for watermarking. We use a convolutional neural network (CNN) to control the embedding strength based on the image content. Different image processing attacks are simulated as a network layer to improve the robustness of the model.
  arXiv  Detail & Related papers  (2021-05-24T05:18:33Z)
• WDNet: Watermark-Decomposition Network for Visible Watermark Removal [61.14614115654322]
  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.
  arXiv  Detail & Related papers  (2020-12-14T15:07:35Z)
• 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)
• An Automated and Robust Image Watermarking Scheme Based on Deep Neural Networks [8.765045867163648]
  A robust and blind image watermarking scheme based on deep learning neural networks is proposed. The robustness of the proposed scheme is achieved without requiring any prior knowledge or adversarial examples of possible attacks.
  arXiv  Detail & Related papers  (2020-07-05T22:23:31Z)

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.