{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T18:31:11Z","timestamp":1772303471544,"version":"3.50.1"},"reference-count":44,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T00:00:00Z","timestamp":1689120000000},"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":"crossref","award":["U1804263, 62172435"],"award-info":[{"award-number":["U1804263, 62172435"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the Zhongyuan Science and Technology Innovation Leading Talent Project, China","award":["214200510019"],"award-info":[{"award-number":["214200510019"]}]},{"name":"National Key Research and Development Program of China","award":["2022YFB3102900"],"award-info":[{"award-number":["2022YFB3102900"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2023,11,30]]},"abstract":"<jats:p>\n            With the rapid proliferation of urbanization, massive data in social networks are collected and aggregated in real time, making it possible for criminals to use images as a cover to spread secret information on the Internet. How to determine whether these images contain secret information is a huge challenge for multimedia computing security. The steganalysis method based on deep learning can effectively judge whether the pictures transmitted on the Internet in urban scenes contain secret information, which is of great significance to safeguarding national and social security. Image steganalysis based on deep learning has powerful learning ability and classification ability, and its detection accuracy of steganography images has surpassed that of traditional steganalysis based on manual feature extraction. In recent years, it has become a hot topic of the information hiding technology. However, the detection accuracy of existing deep learning based steganalysis methods still needs to be improved, especially when detecting arbitrary-size and multi-source images, their detection efficientness is easily affected by cover mismatch. In this manuscript, we propose a steganalysis method based on Inverse Residuals structured Siamese network (abbreviated as\n            <jats:bold>SiaIRNet<\/jats:bold>\n            method,\n            <jats:bold>\n              <jats:underline>Sia<\/jats:underline>\n              mese-\n              <jats:underline>I<\/jats:underline>\n              nverted-\n              <jats:underline>R<\/jats:underline>\n              esiduals-\n              <jats:underline>Net<\/jats:underline>\n              work\n            <\/jats:bold>\n            Based method). The SiaIRNet method uses a siamese\n            <jats:bold>convolutional neural network (CNN)<\/jats:bold>\n            to obtain the residual features of subgraphs, including three stages of preprocessing, feature extraction, and classification. Firstly, a preprocessing layer with high-pass filters combined with depth-wise separable convolution is designed to more accurately capture the correlation of residuals between feature channels, which can help capture rich and effective residual features. Then, a feature extraction layer based on the Inverse Residuals structure is proposed, which improves the ability of the model to obtain residual features by expanding channels and reusing features. Finally, a fully connected layer is used to classify the cover image and the stego image features. Utilizing three general datasets, BossBase-1.01, BOWS2, and ALASKA#2, as cover images, a large number of experiments are conducted comparing with the state-of-the-art steganalysis methods. The experimental results show that compared with the classical SID method and the latest SiaStegNet method, the detection accuracy of the proposed method for 15 arbitrary-size images is improved by 15.96% and 5.86% on average, respectively, which verifies the higher detection accuracy and better adaptability of the proposed method to multi-source and arbitrary-size images in urban scenes.\n          <\/jats:p>","DOI":"10.1145\/3579166","type":"journal-article","created":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T12:04:52Z","timestamp":1673438692000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["A Siamese Inverted Residuals Network Image Steganalysis Scheme based on Deep Learning"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7506-1460","authenticated-orcid":false,"given":"Hao","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory of Mathematical Engineering and Advanced Computing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9366-5671","authenticated-orcid":false,"given":"Jinwei","family":"Wang","sequence":"additional","affiliation":[{"name":"Nanjing University of Information Science &amp; Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0394-4635","authenticated-orcid":false,"given":"Neal","family":"Xiong","sequence":"additional","affiliation":[{"name":"Sul Ross State University, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1832-1235","authenticated-orcid":false,"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mathematical Engineering and Advanced Computing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1902-9877","authenticated-orcid":false,"given":"Athanasios V.","family":"Vasilakos","sequence":"additional","affiliation":[{"name":"University of Agder, Norway and Fuzhou University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3225-4649","authenticated-orcid":false,"given":"Xiangyang","family":"Luo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mathematical Engineering and Advanced Computing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,7,12]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3005348"},{"key":"e_1_3_1_3_2","first-page":"59","volume-title":"International Workshop on Information Hiding","author":"Bas Patrick","year":"2011","unstructured":"Patrick Bas, Tom\u00e1\u0161 Filler, and Tom\u00e1\u0161 Pevn\u1ef3. 2011. Break our steganographic system: The ins and outs of organizing BOSS. In International Workshop on Information Hiding. Springer, 59\u201370."},{"key":"e_1_3_1_4_2","volume-title":"BOWS-2","author":"Bas Patrick","year":"2022","unstructured":"Patrick Bas and Teddy Furon. 2022. BOWS-2. Retrieved February 17, 2022 from http:\/\/bows2.ec-lille.fr\/."},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2018.2871749"},{"issue":"2","key":"e_1_3_1_6_2","first-page":"551","article-title":"Steganalysis based on deep learning:A review","volume":"32","author":"Chen Junfu","year":"2021","unstructured":"Junfu Chen, Zhangjie Fu, Weiming Zhang, Xu Cheng, and Xingming Sun. 2021. Steganalysis based on deep learning:A review. J. Softw. 32, 2 (2021), 551\u2013745.","journal-title":"J. Softw."},{"issue":"3","key":"e_1_3_1_7_2","article-title":"Introduction to the special issue on explainable AI on multimedia computing","volume":"17","author":"Cheng Wenhuang","year":"2021","unstructured":"Wenhuang Cheng, Jiaying Liu, Nicu Sebe, Junsong Yuan, and Honghan Shuai. 2021. Introduction to the special issue on explainable AI on multimedia computing. ACM Trans. Multimedia Comput. Commun. Appl. 17, 3s (2021).","journal-title":"ACM Trans. Multimedia Comput. Commun. Appl."},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.195"},{"key":"e_1_3_1_9_2","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1145\/3335203.3335726","volume-title":"Proceedings of the ACM Workshop on Information Hiding and Multimedia Security","author":"Cogranne R\u00e9mi","year":"2019","unstructured":"R\u00e9mi Cogranne, Quentin Giboulot, and Patrick Bas. 2019. The ALASKA steganalysis challenge: A first step towards steganalysis. In Proceedings of the ACM Workshop on Information Hiding and Multimedia Security. 125\u2013137."},{"key":"e_1_3_1_10_2","volume-title":"Documentation of AlaskaV2 Dataset Scripts: A Hint Moving Towards Steganography and Steganalysis into the Wild","author":"Cogranne R\u00e9mi","year":"2022","unstructured":"R\u00e9mi Cogranne, Quentin Giboulot, and Patrick Bas. 2022. Documentation of AlaskaV2 Dataset Scripts: A Hint Moving Towards Steganography and Steganalysis into the Wild. Retrieved February 17, 2022 from https:\/\/alaska.utt.fr."},{"key":"e_1_3_1_11_2","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1007\/978-3-642-24178-9_13","volume-title":"International Workshop on Information Hiding","author":"Cogranne R\u00e9mi","year":"2011","unstructured":"R\u00e9mi Cogranne, Cathel Zitzmann, Lionel Fillatre, Florent Retraint, Igor Nikiforov, and Philippe Cornu. 2011. A cover image model for reliable steganalysis. In International Workshop on Information Hiding. Springer, 178\u2013192."},{"key":"e_1_3_1_12_2","first-page":"355","volume-title":"International Workshop on Information Hiding","author":"Dumitrescu Sorina","year":"2002","unstructured":"Sorina Dumitrescu, Xiaolin Wu, and Zhe Wang. 2002. Detection of LSB steganography via sample pair analysis. In International Workshop on Information Hiding. Springer, 355\u2013372."},{"key":"e_1_3_1_13_2","first-page":"107","article-title":"The square root law of steganographic capacity for Markov covers","volume":"7254","author":"Filler Tom\u00e1\u0161","year":"2009","unstructured":"Tom\u00e1\u0161 Filler, Andrew D. Ker, and Jessica Fridrich. 2009. The square root law of steganographic capacity for Markov covers. Proceedings of SPIE the International Society for Optical Engineering 7254, 107\u2013116.","journal-title":"Proceedings of SPIE the International Society for Optical Engineering"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/1232454.1232466"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2012.2190402"},{"key":"e_1_3_1_16_2","doi-asserted-by":"crossref","first-page":"5708C5709","DOI":"10.1145\/3475731","volume-title":"Trustworthy AI\u201921: 1st International Workshop on Trustworthy AI for Multimedia Computing","author":"Furon Teddy","year":"2021","unstructured":"Teddy Furon, Jingen Liu, Yogesh Rawat, Wei Zhang, and Qi Zhao. 2021. Trustworthy AI\u201921: 1st International Workshop on Trustworthy AI for Multimedia Computing. Association for Computing Machinery, New York, NY, USA, 5708C5709."},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_1_18_2","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1109\/WIFS.2012.6412655","volume-title":"2012 IEEE International Workshop on Information Forensics and Security (WIFS)","author":"Holub Vojt\u011bch","year":"2012","unstructured":"Vojt\u011bch Holub and Jessica Fridrich. 2012. Designing steganographic distortion using directional filters. In 2012 IEEE International Workshop on Information Forensics and Security (WIFS). IEEE, 234\u2013239."},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1145\/2482513.2482514"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2013.2286682"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2006.891319"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/1411328.1411349"},{"issue":"2","key":"e_1_3_1_23_2","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1109\/TIFS.2011.2175919","article-title":"Ensemble classifiers for steganalysis of digital media","volume":"7","author":"Kodovsky Jan","year":"2011","unstructured":"Jan Kodovsky, Jessica Fridrich, and Vojt\u011bch Holub. 2011. Ensemble classifiers for steganalysis of digital media. IEEE Trans. Inf. Forensics Security 7, 2 (2011), 432\u2013444.","journal-title":"IEEE Trans. Inf. Forensics Security"},{"key":"e_1_3_1_24_2","first-page":"4206","volume-title":"2014 IEEE International Conference on Image Processing (ICIP)","author":"Li Bin","year":"2014","unstructured":"Bin Li, Ming Wang, Jiwu Huang, and Xiaolong Li. 2014. A new cost function for spatial image steganography. In 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 4206\u20134210."},{"key":"e_1_3_1_25_2","article-title":"Pruning filters for efficient ConvNets","author":"Li Hao","year":"2016","unstructured":"Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf. 2016. Pruning filters for efficient ConvNets. arXiv preprint arXiv:1608.08710 (2016).","journal-title":"arXiv preprint arXiv:1608.08710"},{"issue":"1","key":"e_1_3_1_26_2","first-page":"86C94","article-title":"Information hiding: Challenges for forensic experts","volume":"61","author":"Mazurczyk Wojciech","year":"2017","unstructured":"Wojciech Mazurczyk and Steffen Wendzel. 2017. Information hiding: Challenges for forensic experts. Commun. ACM 61, 1 (2017), 86C94.","journal-title":"Commun. ACM"},{"key":"e_1_3_1_27_2","first-page":"94090I","volume-title":"Media Watermarking, Security, and Forensics 2015","author":"Pevn\u1ef3 Tom\u00e1\u0161","year":"2015","unstructured":"Tom\u00e1\u0161 Pevn\u1ef3 and Andrew D. Ker. 2015. Towards dependable steganalysis. In Media Watermarking, Security, and Forensics 2015, Vol. 9409. 94090I."},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2016.7532860"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_3_1_31_2","first-page":"6105","volume-title":"International Conference on Machine Learning","author":"Tan Mingxing","year":"2019","unstructured":"Mingxing Tan and Quoc Le. 2019. EfficientNet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning. PMLR, 6105\u20136114."},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2020.3005304"},{"issue":"4","key":"e_1_3_1_34_2","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1109\/TIFS.2015.2507159","article-title":"Adaptive steganalysis based on embedding probabilities of pixels","volume":"11","author":"Tang Weixuan","year":"2015","unstructured":"Weixuan Tang, Haodong Li, Weiqi Luo, and Jiwu Huang. 2015. Adaptive steganalysis based on embedding probabilities of pixels. IEEE Trans. Inf. Forensics Security 11, 4 (2015), 734\u2013745.","journal-title":"IEEE Trans. Inf. Forensics Security"},{"issue":"2","key":"e_1_3_1_35_2","article-title":"An image privacy protection algorithm based on adversarial perturbation generative networks","volume":"17","author":"Tong Chao","year":"2021","unstructured":"Chao Tong, Mengze Zhang, Chao Lang, and Zhigao Zheng. 2021. An image privacy protection algorithm based on adversarial perturbation generative networks. ACM Trans. Multimedia Comput. Commun. Appl. 17, 2 (2021).","journal-title":"ACM Trans. Multimedia Comput. Commun. Appl."},{"issue":"7","key":"e_1_3_1_36_2","first-page":"121","article-title":"Steganalyzing images of arbitrary size with CNNs","volume":"2018","author":"Tsang Clement Fuji","year":"2018","unstructured":"Clement Fuji Tsang and Jessica Fridrich. 2018. Steganalyzing images of arbitrary size with CNNs. Electron. Imag. 2018, 7 (2018), 121\u20131.","journal-title":"Electron. Imag."},{"key":"e_1_3_1_37_2","first-page":"103","volume-title":"Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security","author":"Xu Guanshuo","year":"2016","unstructured":"Guanshuo Xu, Hanzhou Wu, and Yunqing Shi. 2016. Ensemble of CNNs for steganalysis: An empirical study. In Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security. 103\u2013107."},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2016.2548421"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2017.2710946"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2018.8461438"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2020.3013204"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1145\/3437880.3460397"},{"key":"e_1_3_1_43_2","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1145\/3335203.3335727","volume-title":"Proceedings of the ACM Workshop on Information Hiding and Multimedia Security","author":"Yousfi Yassine","year":"2019","unstructured":"Yassine Yousfi, Jan Butora, Jessica Fridrich, and Quentin Giboulot. 2019. Breaking ALASKA: Color separation for steganalysis in JPEG domain. In Proceedings of the ACM Workshop on Information Hiding and Multimedia Security. 138\u2013149."},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2019.2936913"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2021.02.058"}],"container-title":["ACM Transactions on Multimedia Computing, Communications, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3579166","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3579166","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:49:27Z","timestamp":1750182567000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3579166"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,12]]},"references-count":44,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,11,30]]}},"alternative-id":["10.1145\/3579166"],"URL":"https:\/\/doi.org\/10.1145\/3579166","relation":{},"ISSN":["1551-6857","1551-6865"],"issn-type":[{"value":"1551-6857","type":"print"},{"value":"1551-6865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,12]]},"assertion":[{"value":"2022-02-28","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-12-23","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-07-12","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}