[1] [1] GUGELMANN D, SOMMER D, LENDERS V, et al. Screen watermarking for data theft investigation and attribution[C]//2018 10th International Conference on Cyber Conflict (CyCon), May 29-June 1, 2018, Tallinn, Estonia. New York: IEEE, 2018: 391-408.

[2] [2] HAN S, YANG J, WANG R, et al. A robust color image watermarking algorithm against rotation attacks[J]. Optoelectronicsletters, 2018, 14(1): 61-66.

[3] [3] KANG X, HUANG J, ZENG W. Efficient general print-scanning resilient data hiding based on uniform log-polar mapping[J]. IEEE transactions on information forensics and security, 2010, 5(1): 1-12.

[4] [4] NAKAMURA T, KATAYAMA A, YAMAMURO M, et al. Fast watermark detection scheme from camera-captured images on mobile phones[J]. International journal of pattern recognition and artificial intelligence, 2006, 20(04): 543-564.

[5] [5] SCHABER P, KOPF S, WETZEL S, et al. CamMark: analyzing, modeling, and simulating artifacts in camcorder copies[J]. ACM transactions on multimedia computing, communications, and applications (TOMM), 2015, 11(2s): 1-23.

[6] [6] FANG H, ZHANG W, ZHOU H, et al. Screen-shooting resilient watermarking[J]. IEEE transactions on information forensics and security, 2018, 14(6): 1403-1418.

[7] [7] FANG H, CHEN D, WANG F, et al. TERA: screen-to-camera image code with transparency, efficiency, robustness and adaptability[J]. IEEE transactions on multimedia, 2022, 24: 955-967.

[8] [8] FANG H, JIA Z, ZHOU H, et al. Encoded feature enhancement in watermarking network for distortion in real scenes[J]. IEEE transactions on multimedia, 2023, 25: 2648-2660.

[9] [9] GU W, CHANG C C, BAI Y, et al. Anti-screenshot watermarking algorithm for archival image based on deep learning model[J]. Entropy, 2023, 25(2): 288.

[10] [10] GE S, FEI J, XIA Z, et al. A screen-shooting resilient document image watermarking scheme using deep neural network[J]. IET image processing, 2023, 17(2): 323-336.

[11] [11] FANG H, JIA Z, MA Z, et al. PIMoG: an effective screen-shooting noise-layer simulation for deep-learning-based watermarking network[C]//Proceedings of the 30th ACM International Conference on Multimedia, October 10-14, 2022, Lisboa,Portugal. New York: ACM, 2022: 2267-2275.

[12] [12] ZHANG C, KARJAUV A, BENZ P, et al. Towards robust deep hiding under non-differentiable distortions for practical blind watermarking[C]//Proceedings of the 29th ACM International Conference on Multimedia, October 20-24, 2021, Virtual. New York: ACM, 2021: 5158-5166.

[13] [13] ZHAO T, SUN Y, Lü X, et al. Deep learning-based channel estimation for wireless ultraviolet MIMO communication systems[J]. Optoelectronics letters, 2024, 20(1): 35-41.

[14] [14] TANCIK M, MILDENHALL B, NG R. StegaStamp: invisible hyperlinks in physical photographs[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 13-19, 2020, Seattle, WA, USA. New York: IEEE, 2020: 2117-2126.

[15] [15] ZHU J, KAPLAN R, JOHNSON J, et al. HiDDeN: hiding data with deep networks[C]//Proceedings of the European Conference on Computer Vision (ECCV), September 8-14, 2018, Munich, Germany. Berlin, Heidelberg:Springer, 2018: 657-672.

[16] [16] LIU Y, GUO M, ZHANG J, et al. A novel two-stage separable deep learning framework for practical blind watermarking[C]//Proceedings of the 27th ACM International Conference on Multimedia, October 21-25, 2019, Nice, France. New York: ACM, 2021: 1509-1517.

[17] [17] JéGOU S, DROZDZAL M, VAZQUEZ D, et al. The one hundred layers tiramisu: fully convolutional densenets for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, July 21-26, 2017, Honolulu, Hawaii, USA. New York: IEEE, 2017: 11-19.

[18] [18] HUANG G, LIU Z, VAN DER MAATEN L, et al. Densely connected convolutional networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, July 21-26, 2017, Honolulu, Hawaii, USA. New York: IEEE, 2017: 4700-4708.

[19] [19] LIU F, YANG J, YUE H. Moiré pattern removal from texture images via low-rank and sparse matrix decomposition[C]//2015 Visual Communications and Image Processing (VCIP), December 13-16, 2015, Singapore. New York: IEEE, 2015: 1-4.

[20] [20] COLLOBERT R, KAVUKCUOGLU K, FARABET C. Torch7: a Matlab-like environment for machine learning[C]//BigLearn NIPS workshop, January, 2011. CiteSeer,2011.

[21] [21] KINGMA D P, BA J. Adam: a method for stochastic optimization[EB/OL]. (2014-12-22) [2023-09-28]. https://arxiv.org/abs/1412.6980v6.

[22] [22] LIN T Y, MAIRE M, BELONGIE S, et al. Microsoft coco: common objects in context[C]//13th European Conference on Computer Vision, September 6-12, 2014, Zurich, Switzerland. Berlin, Heidelberg: Springer, 2014: 740-755.

[23] [23] ANDRILUKA M, PISHCHULIN L, GEHLER P, et al. 2D human pose estimation: new benchmark and state of the art analysis[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, Ohio, USA. New York: IEEE, 2014: 3686-3693.

[24] [24] HUISKES M J, LEW M S. The MIRFLICKR retrieval evaluation[C]//Proceedings of the 1st ACM InternationalConference on Multimedia Information Retrieval, October 30-31, 2008, Vancouver, Canada. New York: ACM, 2008: 39-43.

[25] [25] RONNEBERGER O, FISCHER P, BROX T. U-net: convolutional networks for biomedical image segmentation[C]//18th International Conference on Medical Image Computing and Computer-Assisted Intervention, October 5-9, 2015, Munich, Germany. Berlin, Heidelberg:Springer, 2015: 234-241.