{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T13:57:01Z","timestamp":1774965421998,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":48,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T00:00:00Z","timestamp":1665360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U20B2051; 62072114; U20A20178; U1936214"],"award-info":[{"award-number":["U20B2051; 62072114; U20A20178; U1936214"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,10]]},"DOI":"10.1145\/3503161.3548217","type":"proceedings-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T15:42:35Z","timestamp":1665416555000},"page":"1621-1629","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":57,"title":["Generative Steganography Network"],"prefix":"10.1145","author":[{"given":"Ping","family":"Wei","sequence":"first","affiliation":[{"name":"Fudan University, Shanghai, China"}]},{"given":"Sheng","family":"Li","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}]},{"given":"Xinpeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}]},{"given":"Ge","family":"Luo","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}]},{"given":"Zhenxing","family":"Qian","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}]},{"given":"Qing","family":"Zhou","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}]}],"member":"320","published-online":{"date-parts":[[2022,10,10]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2901877"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2008.2007294"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2018.2871749"},{"key":"e_1_3_2_2_4_1","volume-title":"Infogan: Interpretable representation learning by information maximizing generative adversarial nets. arXiv preprint arXiv:1606.03657","author":"Chen Xi","year":"2016","unstructured":"Xi Chen , Yan Duan , Rein Houthooft , John Schulman , Ilya Sutskever , and Pieter Abbeel . 2016 . Infogan: Interpretable representation learning by information maximizing generative adversarial nets. arXiv preprint arXiv:1606.03657 (2016). Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel. 2016. Infogan: Interpretable representation learning by information maximizing generative adversarial nets. arXiv preprint arXiv:1606.03657 (2016)."},{"key":"e_1_3_2_2_5_1","volume-title":"a master of steganography. arXiv preprint arXiv:1712.02950","author":"Chu Casey","year":"2017","unstructured":"Casey Chu , Andrey Zhmoginov , and Mark Sandler . 2017. Cyclegan , a master of steganography. arXiv preprint arXiv:1712.02950 ( 2017 ). Casey Chu, Andrey Zhmoginov, and Mark Sandler. 2017. Cyclegan, a master of steganography. arXiv preprint arXiv:1712.02950 (2017)."},{"key":"e_1_3_2_2_6_1","volume-title":"Coverless information hiding based on generative model. arXiv preprint arXiv:1802.03528","author":"Duan Xintao","year":"2018","unstructured":"Xintao Duan and Haoxian Song . 2018. Coverless information hiding based on generative model. arXiv preprint arXiv:1802.03528 ( 2018 ). Xintao Duan and Haoxian Song. 2018. Coverless information hiding based on generative model. arXiv preprint arXiv:1802.03528 (2018)."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2011.2134094"},{"key":"e_1_3_2_2_8_1","volume-title":"Steganography in digital media: principles, algorithms, and applications","author":"Fridrich Jessica","unstructured":"Jessica Fridrich . 2009. Steganography in digital media: principles, algorithms, and applications . Cambridge University Press . Jessica Fridrich. 2009. Steganography in digital media: principles, algorithms, and applications. Cambridge University Press."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/WIFS.2014.7084325"},{"key":"e_1_3_2_2_10_1","unstructured":"Jamie Hayes and George Danezis. 2017. Generating steganographic images via adversarial training. In Advances in Neural Information Processing Systems. 1954--1963.  Jamie Hayes and George Danezis. 2017. Generating steganographic images via adversarial training. In Advances in Neural Information Processing Systems. 1954--1963."},{"key":"e_1_3_2_2_11_1","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems. 6629--6640","author":"Heusel Martin","year":"2017","unstructured":"Martin Heusel , Hubert Ramsauer , Thomas Unterthiner , Bernhard Nessler , and Sepp Hochreiter . 2017 . GANs trained by a two time-scale update rule converge to a local nash equilibrium . In Proceedings of the 31st International Conference on Neural Information Processing Systems. 6629--6640 . Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. 2017. GANs trained by a two time-scale update rule converge to a local nash equilibrium. In Proceedings of the 31st International Conference on Neural Information Processing Systems. 6629--6640."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2852771"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.image.2018.03.012"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00469"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2014.7025854"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2878290"},{"key":"e_1_3_2_2_18_1","volume-title":"Coverless information hiding based on generative adversarial networks. arXiv preprint arXiv:1712.06951","author":"Liu Ming-Ming","year":"2017","unstructured":"Ming-Ming Liu , Min-qing Zhang, Jia Liu , Ying-nan Zhang, and Yan Ke. 2017. Coverless information hiding based on generative adversarial networks. arXiv preprint arXiv:1712.06951 ( 2017 ). Ming-Ming Liu, Min-qing Zhang, Jia Liu, Ying-nan Zhang, and Yan Ke. 2017. Coverless information hiding based on generative adversarial networks. arXiv preprint arXiv:1712.06951 (2017)."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01067"},{"key":"e_1_3_2_2_21_1","volume-title":"Which training methods for GANs do actually converge? arXiv preprint arXiv:1801.04406","author":"Mescheder Lars","year":"2018","unstructured":"Lars Mescheder , Andreas Geiger , and Sebastian Nowozin . 2018. Which training methods for GANs do actually converge? arXiv preprint arXiv:1801.04406 ( 2018 ). Lars Mescheder, Andreas Geiger, and Sebastian Nowozin. 2018. Which training methods for GANs do actually converge? arXiv preprint arXiv:1801.04406 (2018)."},{"key":"e_1_3_2_2_22_1","volume-title":"Making a \"completely blind\" image quality analyzer","author":"Mittal Anish","year":"2012","unstructured":"Anish Mittal , Rajiv Soundararajan , and Alan C Bovik . 2012. Making a \"completely blind\" image quality analyzer . IEEE Signal processing letters, Vol. 20 , 3 ( 2012 ), 209--212. Anish Mittal, Rajiv Soundararajan, and Alan C Bovik. 2012. Making a \"completely blind\" image quality analyzer. IEEE Signal processing letters, Vol. 20, 3 (2012), 209--212."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.5555\/3305890.3305954"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCG.2009.127"},{"key":"e_1_3_2_2_25_1","volume-title":"Hide and seek: An introduction to steganography","author":"Provos Niels","year":"2003","unstructured":"Niels Provos and Peter Honeyman . 2003. Hide and seek: An introduction to steganography . IEEE security & privacy, Vol. 1 , 3 ( 2003 ), 32--44. Niels Provos and Peter Honeyman. 2003. Hide and seek: An introduction to steganography. IEEE security & privacy, Vol. 1, 3 (2003), 32--44."},{"key":"e_1_3_2_2_26_1","volume-title":"Advances in Intelligent Information Hiding and Multimedia Signal Processing","author":"Qian Zhenxing","unstructured":"Zhenxing Qian , Hang Zhou , Weiming Zhang , and Xinpeng Zhang . 2017. Robust steganography using texture synthesis . In Advances in Intelligent Information Hiding and Multimedia Signal Processing . Springer , 25--33. Zhenxing Qian, Hang Zhou, Weiming Zhang, and Xinpeng Zhang. 2017. Robust steganography using texture synthesis. In Advances in Intelligent Information Hiding and Multimedia Signal Processing. Springer, 25--33."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2955452"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.3390\/math8091394"},{"key":"e_1_3_2_2_29_1","volume-title":"Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434","author":"Radford Alec","year":"2015","unstructured":"Alec Radford , Luke Metz , and Soumith Chintala . 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 ( 2015 ). Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3053998"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2017.2745572"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2018.2881118"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-04179-3_22"},{"key":"e_1_3_2_2_34_1","first-page":"130","article-title":"Steganography Using Reversible Texture Synthesis","volume":"24","author":"Wu K","year":"2014","unstructured":"K Wu and C Wang . 2014 . Steganography Using Reversible Texture Synthesis . IEEE Transactions on Image Processing , Vol. 24 , 1 (2014), 130 -- 139 . K Wu and C Wang. 2014. Steganography Using Reversible Texture Synthesis. IEEE Transactions on Image Processing, Vol. 24, 1 (2014), 130--139.","journal-title":"IEEE Transactions on Image Processing"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2014.2302899"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-014-1045-z"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2017.2710946"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2019.2895200"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5463"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12083-020-01033-x"},{"key":"e_1_3_2_2_41_1","volume-title":"Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv:1506.03365","author":"Yu Fisher","year":"2015","unstructured":"Fisher Yu , Ari Seff , Yinda Zhang , Shuran Song , Thomas Funkhouser , and Jianxiong Xiao . 2015 . Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv:1506.03365 (2015). Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao. 2015. Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv:1506.03365 (2015)."},{"key":"e_1_3_2_2_42_1","first-page":"10223","article-title":"Udh: Universal deep hiding for steganography, watermarking, and light field messaging","volume":"33","author":"Zhang Chaoning","year":"2020","unstructured":"Chaoning Zhang , Philipp Benz , Adil Karjauv , Geng Sun , and In So Kweon . 2020 a. Udh: Universal deep hiding for steganography, watermarking, and light field messaging . Advances in Neural Information Processing Systems , Vol. 33 (2020), 10223 -- 10234 . Chaoning Zhang, Philipp Benz, Adil Karjauv, Geng Sun, and In So Kweon. 2020a. Udh: Universal deep hiding for steganography, watermarking, and light field messaging. Advances in Neural Information Processing Systems, Vol. 33 (2020), 10223--10234.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_43_1","volume-title":"SteganoGAN: high capacity image steganography with gans. arXiv preprint arXiv:1901.03892","author":"Zhang Kevin Alex","year":"2019","unstructured":"Kevin Alex Zhang , Alfredo Cuesta-Infante , Lei Xu , and Kalyan Veeramachaneni . 2019a. SteganoGAN: high capacity image steganography with gans. arXiv preprint arXiv:1901.03892 ( 2019 ). Kevin Alex Zhang, Alfredo Cuesta-Infante, Lei Xu, and Kalyan Veeramachaneni. 2019a. SteganoGAN: high capacity image steganography with gans. arXiv preprint arXiv:1901.03892 (2019)."},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.26599\/TST.2019.9010027"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.26599\/TST.2019.9010027"},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2920313"},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01267-0_40"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"}],"event":{"name":"MM '22: The 30th ACM International Conference on Multimedia","location":"Lisboa Portugal","acronym":"MM '22","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 30th ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3503161.3548217","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3503161.3548217","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:00:20Z","timestamp":1750186820000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3503161.3548217"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,10]]},"references-count":48,"alternative-id":["10.1145\/3503161.3548217","10.1145\/3503161"],"URL":"https:\/\/doi.org\/10.1145\/3503161.3548217","relation":{},"subject":[],"published":{"date-parts":[[2022,10,10]]},"assertion":[{"value":"2022-10-10","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}