Skip to main content
SpringerLink
Log in
SpringerLink
Log in

Noise Simulation-Based Deep Optical Watermarking

  • Conference paper
  • First Online:
Artificial Intelligence and Security (ICAIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13340))

Included in the following conference series:

Abstract

Digital watermarking is an important branch of information hiding, which effectively guarantees the robustness of embedded watermarks in distorted channels. To embed the watermark into the host carrier, traditional watermarking schemes often require the modification of the carrier. However, in some cases, the modification of the carrier is not allowed such as paintings in museums. To address such limitation, we utilize optical watermarking to embed the watermark into the host carrier. Optical watermarking refers to a technique that encodes the watermark into the visible light irradiating the object, where the watermark can be further extracted by the camera photography process. To realize transparency and robustness of the watermark, we propose a color-decomposition-based watermarking pattern generation algorithm which satisfies human visual system (HVS) characteristics, a camera shooting simulation algorithm which accurately produces the dataset for training, and a decoding network which can realize loss-less decoding of the embedded watermark. Various experiments demonstrate the superiority of our method and reveal the broad applicability of the proposed technique.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Petitcolas, F.A., Anderson, R.J., Kuhn, M.G.: Information hiding-a survey. Proc. IEEE 87(7), 1062–1078 (1999)

    Article  Google Scholar 

  2. Katzenbeisser, S., Petitcolas, F.A.P.: Digital Watermarking, vol. 2. Springer, Artech House, London (2000)

    Google Scholar 

  3. Xiong, L., Han, X., Yang, C., Shi, Y.Q.: Robust reversible watermarking in encrypted image with secure multi-party based on lightweight cryptography. IEEE Trans. Circ. Syst. Video Technol. 32, 75–91 (2021). https://doi.org/10.1109/TCSVT.2021.3055072

  4. Bhaskar, A., Sharma, C., Mohiuddin, K., Singh, A., Nasr, O.A.: A robust video watermarking scheme with squirrel search algorithm. Comput. Mater. Continua 71(2), 3069–3089 (2022)

    Article  Google Scholar 

  5. Pereira, S., Pun, T.: Robust template matching for affine resistant image watermarks. IEEE Trans. Image Process. 9(6), 1123–1129 (2000)

    Article  Google Scholar 

  6. Cox, I.J., Kilian, J., Leighton, F.T., Shamoon, T.: Secure spread spectrum watermarking for multimedia. IEEE Trans. Image Process. 6(12), 1673–1687 (1997)

    Article  Google Scholar 

  7. Alhumyani, H., Alrube, I., Alsharif, S., Afifi, A., Amar, C.B.: Analytic beta-wavelet transform-based digital image watermarking for secure transmission. Comput. Mater. Continua 70(3), 4657–4673 (2022)

    Article  Google Scholar 

  8. Fang, H., Zhang, W., Zhou, H., Cui, H., Yu, N.: Screen-shooting resilient watermarking. IEEE Trans. Inf. Forensics Secur. 14(6), 1403–1418 (2018)

    Article  Google Scholar 

  9. Uehira, K., Suzuki, K., Ikeda, H.: Applications of optoelectronic watermarking technique to new business and industry systems utilizing flat-panel displays and smart devices. In: IEEE Industry Application Society Annual Meeting, pp. 1–9 (2014)

    Google Scholar 

  10. Uehira, K., Suzuki, M.: Digital watermarking technique using brightness-modulated light. In: IEEE International Conference on Multimedia and Expo, pp. 257–260 (2008)

    Google Scholar 

  11. Ishikawa, Y., Uehira, K., Yanaka, K.: Practical evaluation of illumination watermarking technique using orthogonal transforms. J. Display Technol. 6(9), 351–358 (2010)

    Article  Google Scholar 

  12. Uehira, K., Unno, H.: Effects of JPEG compression on reading optical watermarking em-bedded by using color-difference modulation. J. Comput. Commun. 6(1), 56–64 (2017)

    Article  Google Scholar 

  13. Oshita, K., Unno, H., Uehira, K.: Optically written watermarking technology using temporally and spatially luminance-modulated light. Electron. Imaging 2016(8), 1–6 (2016)

    Article  Google Scholar 

  14. Unno, H., Uehira, K.: Lighting technique for attaching invisible information onto real objects using temporally and spatially color-intensity modulated light. IEEE Trans. Ind. Appl. 56(6), 7202–7207 (2020)

    Article  Google Scholar 

  15. Unno, H., Yamkum, R., Bunporn, C., Uehira, K.: A new displaying technology for information hiding using temporally brightness modulated pattern. IEEE Trans. Ind. Appl. 53(1), 596–601 (2016)

    Article  Google Scholar 

  16. Unno, H., Uehira, K.: Display technique for embedding information in real object images using temporally and spatially luminance-modulated light. IEEE Trans. Ind. Appl. 53(6), 5966–5971 (2017)

    Article  Google Scholar 

  17. Cui, H., Bian, H., Zhang, W., Yu, N.: Unseencode: invisible on-screen barcode with image-based extraction. In: IEEE Conference on Computer Communications, pp. 1315–1323 (2019)

    Google Scholar 

  18. Zhang, L., et al.: Kaleido: you can watch it but cannot record it. In: Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, pp. 372–385 (2015)

    Google Scholar 

  19. Song, K., Liu, N., Gao, Z., Zhang, J., Zhai, G., Zhang, X.P.: Deep restoration of invisible QR code from TPVM display. In: 2020 IEEE International Conference on Multimedia & Expo Workshops, pp. 1–6 (2020)

    Google Scholar 

  20. Nomura, Y., et al.: Evaluation of critical flicker-fusion frequency measurement methods using a touchscreen-based visual temporal discrimination task in the behaving mouse. Neurosci. Res. 148, 28–33 (2019)

    Google Scholar 

  21. Menozzi, M., Lang, F., Naepflin, U., Zeller, C., Krueger, H.: CRT versus LCD: effects of refresh rate, display technology and background luminance in visual performance. Displays 22(3), 79–85 (2001)

    Article  Google Scholar 

  22. Liu, Y., Li, Y., You, S., Lu, F.: Unsupervised learning for intrinsic image decomposition from a single image. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3248–3257 (2020)

    Google Scholar 

  23. Land, E.H., Mccann, J.J.: Lightness and retinex theory. Josa 61(1), 1–11 (1971)

    Article  Google Scholar 

  24. Grosse, R., Johnson, M.K., Adelson, E.H., Freeman, W.T.: Ground truth dataset and baseline evaluations for intrinsic image algorithms. In: Proceedings of the IEEE Conference on Computer Vision, pp. 2335–2342 (2009)

    Google Scholar 

  25. Gugelmann, D., Sommer, D., Lenders, V., Happe, M., Vanbever, L.: Screen watermarking for data theft investigation and attribution. In: 10th International Conference on Cyber Conflict, pp. 391–408 (2018)

    Google Scholar 

  26. Fang, H., et al.: Deep template-based watermarking. IEEE Trans. Circ. Syst. Video Technol. 31(4), 1436–1451 (2020)

    Google Scholar 

  27. Katayama, A., Nakamura, T., Yamamuro, M., Sonehara, N.: New high-speed frame detection method: Side trace algorithm (STA) for i-appli on cellular phones to detect watermarks. In: Proceedings of the 3rd International Conference on Mobile and Ubiquitous Multimedia, pp. 109–116 (2004)

    Google Scholar 

  28. Tancik, M., Mildenhall, B., Ng, R.: Stegastamp: invisible hyperlinks in physical photographs. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2117–2126 (2020)

    Google Scholar 

  29. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  30. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (2014)

    Google Scholar 

  31. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Li, F.F.: ImageNet: a large-scale hierarchical image database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2009)

    Google Scholar 

  32. The USC-SIPI image database (2020). http://sipi.usc.edu/database/

Download references

Funding

This work was supported in part by the Natural Science Foundation of China under Grant 62072421, 62002334, 62102386, 62121002 and U20B2047, Anhui Science Foundation of China under Grant 2008085QF296, Exploration Fund Project of University of Science and Technology of China under Grant YD3480002001, and by Fundamental Research Funds for the Central Universities WK5290000001.

Author information

Authors and Affiliations

  1. University of Science and Technology of China, Hefei, China

    Feng Wang, Weiming Zhang & Nenghai Yu

  2. Simon Fraser University, Vancouver, Canada

    Hang Zhou

  3. National University of Singapore, Singapore, Singapore

    Han Fang

Authors
  1. Feng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hang Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Han Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Weiming Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nenghai Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Weiming Zhang .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Xiaorui Zhang

  3. Jinan University, Guangzhou, China

    Zhihua Xia

  4. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, F., Zhou, H., Fang, H., Zhang, W., Yu, N. (2022). Noise Simulation-Based Deep Optical Watermarking. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13340. Springer, Cham. https://doi.org/10.1007/978-3-031-06791-4_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-06791-4_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06790-7

  • Online ISBN: 978-3-031-06791-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation