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)


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.


Visible watermark Watermark detection Watermark removal Deep convolutional networks 



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.


  1. 1.
    Santoyo-Garcia, H., Fragoso-Navarro, E., Reyes-Reyes, R., et al.: An automatic visible watermark detection method using total variation. In: IWBF 2017 (2017)Google Scholar
  2. 2.
    Pei, S.C., Zeng, Y.C.: A novel Image recovery algorithm for visible watermarked images. IEEE Trans. Inf. Forensics Secur. 1, 543–550 (2006)CrossRefGoogle Scholar
  3. 3.
    Huang, C.H., Wu, J.L.: Attacking visible watermarking schemes. IEEE Trans. Multimed. 6(1), 16–30 (2004)CrossRefGoogle Scholar
  4. 4.
    Dekel, T., Rubinstein, M., Liu, C., et al.: On the effectiveness of visible watermarks. In: CVPR 2017 (2017)Google Scholar
  5. 5.
    Xu, C., Lu, Y., Zhou, Y.: An automatic visible watermark removal technique using image inpainting algorithms. In: ICSAI 2017 (2017)Google Scholar
  6. 6.
    Qin, C., He, Z., Yao, H.: Visible watermark removal scheme based on reversible data hiding and image inpainting. Sig. Process.: Image Commun. 60, 160–172 (2018)Google Scholar
  7. 7.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: MICCAI 2015 (2015)Google Scholar
  8. 8.
    Girshick, R., Donahue, J., Darrell, T., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR 2014 (2014)Google Scholar
  9. 9.
    Girshick, R.: Fast R-CNN. In: ICCV 2015 (2015)Google Scholar
  10. 10.
    Ren, S., He, K., Girshick, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS 2015 (2015)Google Scholar
  11. 11.
    Redmon, J., Divvala, S., Girshick, R., et al.: You only look once: unified, real-time object detection. In: CVPR 2016 (2016)Google Scholar
  12. 12.
    Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: CVPR 2017 (2017)Google Scholar
  13. 13.
    Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. arXiv preprint (2018)Google Scholar
  14. 14.
    Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). Scholar
  15. 15.
    Lin, T.Y., Goyal, P., Girshick, R., et al.: Focal loss for dense object detection. In: ICCV 2017 (2017)Google Scholar
  16. 16.
    Isola, P., Zhu, J.Y., Zhou, T., et al.: Image-to-image translation with conditional adversarial networks. In: CVPR 2017 (2017)Google Scholar
  17. 17.
    Pathak, D., Krahenbuhl, P., Donahue, J., et al.: Context encoders: feature learning by inpainting. In: CVPR 2016 (2016)Google Scholar
  18. 18.
    Everingham, M., Eslami, S.M.A., Van Gool, L.: The pascal visual object classes challenge: a retrospective. IJCV 111(1), 98–136 (2015)CrossRefGoogle Scholar
  19. 19.
    Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694–711. Springer, Cham (2016). Scholar

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

Personalised recommendations