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

  • Danni Cheng
  • Xiang LiEmail author
  • Wei-Hong Li
  • Chan Lu
  • Fake Li
  • Hua Zhao
  • Wei-Shi Zheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11258)

Abstract

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.

Keywords

Visible watermark Watermark detection Watermark removal Deep convolutional networks 

Notes

Acknowledgment

Danni Cheng and Xiang Li equally contributed to this work. The authors would like to thank Dongcheng Huang and Xiaobin Chang’s valuable advice on paper writing.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Danni Cheng
    • 1
  • Xiang Li
    • 1
    Email author
  • Wei-Hong Li
    • 2
  • Chan Lu
    • 1
  • Fake Li
    • 1
  • Hua Zhao
    • 1
  • Wei-Shi Zheng
    • 2
  1. 1.Ctrip GroupShanghaiChina
  2. 2.Sun Yat-sen UniversityGuangdongChina

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