{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T06:02:41Z","timestamp":1671688961634},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643683683","type":"print"},{"value":"9781643683690","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T00:00:00Z","timestamp":1670889600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,12,13]]},"abstract":"<jats:p>Steganalysis based on deep learning has made noticeable progress over the past few years where the training is all based on paired images. However, scenes without paired training data exist. We present an architecture for learning to generate corresponding pseudo stego image from a cover-image in the absence of paired training images. We seek a mapping G that can generate pseudo stego images indistinguishable from the real but unpaired stego images using an adversarial loss. Because this mapping is highly under-constrained, we designed a CycleGAN and introduce spectrum of stego images to reinforce the adversarial loss. Qualitative comparisons demonstrate the superiority of our approach.<\/jats:p>","DOI":"10.3233\/faia220536","type":"book-chapter","created":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T07:59:45Z","timestamp":1671609585000},"source":"Crossref","is-referenced-by-count":0,"title":["A Generative Learning Architecture Based on CycleGAN for Steganalysis with Unpaired Training Images"],"prefix":"10.3233","author":[{"given":"Han","family":"Zhang","sequence":"first","affiliation":[{"name":"Equipment Management and UAV Engineering College of Air Force Engineering University, Xi\u2019an, China"}]},{"given":"Zhihua","family":"Song","sequence":"additional","affiliation":[{"name":"Air and Missile Defense College of Air Force Engineering University, Xi\u2019an, China"}]},{"given":"Feng","family":"Chen","sequence":"additional","affiliation":[{"name":"Air and Missile Defense College of Air Force Engineering University, Xi\u2019an, China"}]},{"given":"Xiangyang","family":"Lin","sequence":"additional","affiliation":[{"name":"Air and Missile Defense College of Air Force Engineering University, Xi\u2019an, China"}]},{"given":"Qinghua","family":"Xing","sequence":"additional","affiliation":[{"name":"Air and Missile Defense College of Air Force Engineering University, Xi\u2019an, China"}]},{"given":"Qingbo","family":"Zhang","sequence":"additional","affiliation":[{"name":"Air and Missile Defense College of Air Force Engineering University, Xi\u2019an, China"}]},{"given":"Yongmei","family":"Zhao","sequence":"additional","affiliation":[{"name":"Equipment Management and UAV Engineering College of Air Force Engineering University, Xi\u2019an, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Proceedings of CECNet 2022"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA220536","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T07:59:45Z","timestamp":1671609585000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA220536"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,13]]},"ISBN":["9781643683683","9781643683690"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia220536","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,13]]}}}