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ED-END: robust watermarking technology based on deep coupling of feature extractors

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Abstract

In recent years, deep learning-based watermarking methods have been developed to address the shortcomings of traditional watermarking algorithms. Some methods adopt an end-to-end framework to train the watermarking model, enabling excellent watermark embedding and extraction. However, the visual quality and robustness of these approaches remain insufficient, especially ignoring the adequacy of image feature extraction and the correlation between network modules. We propose a feature extractor and decoder deep coupled watermark network, which can help generate high-robust watermarked images. Specifically, a down-sampling feature extractor is employed to supplement image features post-decoder, the extracted features are synchronously provided to the encoder for watermark embedding. Additionally, skip-connection is introduced to share each layer feature information of the decoder with the encoder, thereby improving the correlation between network modules. Comprehensive experimental results show that the proposed scheme can achieve high robustness against screen-shooting and paper printing processes while maintaining the visual quality of the watermarked image.

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Data Availability and Access

The authors confirm that the data supporting the findings of this study are available within the article.

References

  1. Jin C, Wang S-H, Jin S-W, Zhang Q-G, Ye J-M (2009) Robust digital watermark technique for copyright protection. In: 2009 International Symposium on Information Engineering and Electronic Commerce, pp 237–240

  2. Ahmadi SBB, Zhang G, Rabbani M, Boukela L, Jelodar H (2021) An intelligent and blind dual color image watermarking for authentication and copyright protection. Appl Intell 51:1701–1732

    Article  Google Scholar 

  3. Xia Z, Wang X, Wang C, Wang C, Ma B, Li Q, Wang M, Zhao T (2022) A robust zero-watermarking algorithm for lossless copyright protection of medical images. Appl Intell 52(1):607–621

    Article  Google Scholar 

  4. Campbell SL, Gear CW (2014) Color digital watermarking algorithm with invisible and robust balance. Comput Simul 31(3):266–269

    Google Scholar 

  5. Candik M, Brechlerova D (2008) Digital watermarking in digital images. In: 2008 42nd Annual IEEE International Carnahan Conference on Security Technology, pp 43–46

  6. Xie Y, Wang Y, Ma M (2020) Design of a hybrid digital watermarking algorithm with high robustness. J Web Eng 19(5-6):725–746 https://doi.org/10.13052/jwe1540-9589.19567

  7. Namba R, Sakuma J (2019) Robust watermarking of neural network with exponential weighting. In: Proceedings of the 2019 ACM Asia Conference on Computer and Communications Security, Association for Computing Machinery, New York, NY, USA. pp 228–240. https://doi.org/10.1145/3321705.3329808

  8. Dong P, Brankov JG, Galatsanos NP, Yang Y, Davoine F (2005) Digital watermarking robust to geometric distortions. IEEE Trans Image Process 14(12):2140–2150. https://doi.org/10.1109/TIP.2005.857263

    Article  Google Scholar 

  9. Bamatraf A, Ibrahim R, Salleh MNBM (2010) Digital watermarking algorithm using lsb. In: 2010 International Conference on Computer Applications and Industrial Electronics, pp 155–159

  10. Soni GK, Rawat A, Jain S, Sharma SK (2020) A pixel-based digital medical images protection using genetic algorithm with lsb watermark technique. In: Somani AK, Shekhawat RS, Mundra A, Srivastava S, Verma VK (eds) Smart Systems and IoT: Innovations in Computing. Springer, Singapore, pp 483–492

    Google Scholar 

  11. Mahdieh Ghazvini EMH, Mirzadi M (2017) An improved image watermarking method in frequency domain. J Appl Secur Res 12(2):260–275. https://doi.org/10.1080/19361610.2017.1277878

  12. Moosazadeh M, Ekbatanifard G (2019) A new dct-based robust image watermarking method using teaching-learning-based optimization. J Inf Secur Appl 47:28–38

    Google Scholar 

  13. Begum M, Ferdush J, Uddin MS (2022) A hybrid robust watermarking system based on discrete cosine transform, discrete wavelet transform, and singular value decomposition. J King Saud Univer-Comput Inf Sci 34(8):5856–5867

    Article  Google Scholar 

  14. Kang X, Huang J, Shi YQ, Lin Y (2003) A dwt-dft composite watermarking scheme robust to both affine transform and jpeg compression. IEEE Trans Circ Syst Video Technol 13(8):776–786. https://doi.org/10.1109/TCSVT.2003.815957

    Article  Google Scholar 

  15. Sadreazami H, Amini M (2018) A robust image watermarking scheme using local statistical distribution in the contourlet domain. IEEE Trans Circ Syst II: Exp Briefs 66(1):151–155

    Google Scholar 

  16. Ge XZFJ S (2023) A robust document image watermarking scheme using deep neural network. Multimed Tools Appl 82:38589–38612. https://doi.org/10.1007/s11042-023-15048-y

  17. Fang H, Jia Z, Zhou H, Ma Z, Zhang W (2023) Encoded feature enhancement in watermarking network for distortion in real scenes. IEEE Trans Multimed 25:2648–2660. https://doi.org/10.1109/TMM.2022.3149641

  18. Ye G, Gao J, Yin B, Xie W, Wei X (2023) Deep boosting robustness of dnn-based image watermarking via dbmark. In: 2023 International Conference on Culture-Oriented Science and Technology (CoST), pp 186–191 . https://doi.org/10.1109/CoST60524.2023.00046

  19. Wen B, Aydore S (2019) Romark: A robust watermarking system using adversarial training. arXiv:1910.01221

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

  21. Liu Y, Guo M, Zhang J, Zhu Y, Xie X (2019) A novel two-stage separable deep learning framework for practical blind watermarking. In: Proceedings of the 27th ACM International Conference on Multimedia, pp 1509–1517. Association for Computing Machinery, New York, NY, USA . https://doi.org/10.1145/3343031.3351025

  22. Jia J, Gao Z, Chen K, Hu M, Min X, Zhai G, Yang X (2022) Rihoop: Robust invisible hyperlinks in offline and online photographs. IEEE Trans Cybern 52(7):7094–7106. https://doi.org/10.1109/TCYB.2020.3037208

    Article  Google Scholar 

  23. Fang H, Jia Z, Qiu Y, Zhang J, Zhang W, Chang E-C (2023) De-end: Decoder-driven watermarking network. IEEE Trans Multimed 25:7571–7581. https://doi.org/10.1109/TMM.2022.3223559

  24. Qin H, Wang J, Mao X, Zhao Z, Gao X, Lu W (2024) An improved faster r-cnn method for landslide detection in remote sensing images. J Geovisual Spatial Anal 8(1):2

    Article  Google Scholar 

  25. Zhao Y, Wang G, Yang J, Li T, Li Z (2024) Au3-gan: A method for extracting roads from historical maps based on an attention generative adversarial network. J Geovisual Spatial Anal 8(2):26

    Article  Google Scholar 

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

  27. Pramila A, Keskinarkaus A, Seppänen T (2018) Increasing the capturing angle in print-cam robust watermarking. J Syst Software 135:205–215

    Article  Google Scholar 

  28. Wengrowski E, Yuan W, Dana KJ, Ashok A, Gruteser M, Mandayam N (2016) Optimal radiometric calibration for camera-display communication. In: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), pp 1–10 . IEEE

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

    Article  Google Scholar 

  30. Zhu J, Kaplan R, Johnson J, Fei-Fei L (2018) Hidden: Hiding data with deep networks. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds) Computer Vision - ECCV 2018. Springer, Cham, pp 682–697

    Chapter  Google Scholar 

  31. Athalye A, Engstrom L, Ilyas A, Kwok K (2018) Synthesizing robust adversarial examples. In: International Conference on Machine Learning, pp 284–293 . PMLR

  32. Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF (eds) Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015. Springer, Cham, pp 234–241

    Google Scholar 

  33. Zhang P, Li C, Wang C (2020) Viscode: Embedding information in visualization images using encoder-decoder network. IEEE Trans Visual Comput Graph 27(2):326–336

    Article  Google Scholar 

  34. Jia Z, Fang H, Zhang W (2021) Mbrs: Enhancing robustness of dnn-based watermarking by mini-batch of real and simulated jpeg compression. In: Proceedings of the 29th ACM International Conference on Multimedia, pp 41–49

  35. Hasinoff SW (2021) In: Ikeuchi, K. (ed.) Photon, Poisson Noise, pp 980–982. Springer, Cham. https://doi.org/10.1007/978-3-030-63416-2_482

  36. Xie Z, Huang Y, Zhu Y, Jin L, Liu Y, Xie L (2019) Aggregation cross-entropy for sequence recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 6538–6547

  37. Zhang R, Isola P, Efros AA, Shechtman E, Wang O (2018) The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 586–595

  38. Huiskes MJ, Lew MS (2008) The mir flickr retrieval evaluation. In: Proceedings of the 1st ACM International Conference on Multimedia Information Retrieval, pp 39–43. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/1460096.1460104

  39. Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv:1412.6980

  40. Huynh-Thu Q (2008) Scope of validity of psnr in image/video quality assessment. Electron Lett 44:800–8011

    Article  Google Scholar 

  41. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612. https://doi.org/10.1109/TIP.2003.819861

    Article  Google Scholar 

  42. Plataniotis KN (2001) Color image processing and applications. Measure Sci Technol 12(2):222–222

    Article  Google Scholar 

  43. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60:91–110 https://doi.org/10.1023/B:VISI.0000029664.99615.94

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant Nos.62062044) and the Science Foundation of Jiangxi Provincial Department of Education (Grant Nos. GJJ2400903).

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Author notes

  1. Yixiang Fang, Yi Zhao, Kangkang Xu and Junxiang Wang contributed equally to this work.

Authors and Affiliations

  1. Mechatronics and Thermal Engineering R&D Center, China Light Industry Ceramic Research Institute, Jingdezhen, 333001, Jiangxi, China

    Jun Li

  2. School of Mechanical and Electronic Engineering, Jingdezhen Ceramic University, Jingdezhen, 333403, Jiangxi, China

    Yixiang Fang, Yi Zhao, Kangkang Xu & Junxiang Wang

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  1. Jun Li
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  2. Yixiang Fang
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  3. Yi Zhao
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  4. Kangkang Xu
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  5. Junxiang Wang
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Contributions

Jun Li, Yixiang Fang, Junxiang Wang contributed to the conception of the study; Jun Li performed the experiment; Jun Li, Yixiang Fang contributed significantly to analysis and wrote the manuscript; Yixiang Fang, Yi Zhao and Kangkang Xu performed the data analyses; Junxiang Wang helped perform the analysis with constructive discussions.

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Correspondence to Jun Li or Yi Zhao.

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Li, J., Fang, Y., Zhao, Y. et al. ED-END: robust watermarking technology based on deep coupling of feature extractors. Appl Intell 55, 410 (2025). https://doi.org/10.1007/s10489-025-06333-4

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