{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T08:12:12Z","timestamp":1773821532149,"version":"3.50.1"},"reference-count":31,"publisher":"Tech Science Press","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2025]]},"DOI":"10.32604\/cmc.2025.067252","type":"journal-article","created":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T03:54:28Z","timestamp":1751601268000},"page":"1631-1651","source":"Crossref","is-referenced-by-count":2,"title":["Image Steganalysis Based on an Adaptive Attention Mechanism and Lightweight DenseNet"],"prefix":"10.32604","volume":"85","author":[{"given":"Zhenxiang","family":"He","sequence":"first","affiliation":[]},{"given":"Rulin","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Xinyuan","family":"Wang","sequence":"additional","affiliation":[]}],"member":"17807","published-online":{"date-parts":[[2025]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","DOI":"10.1561\/116.20240038","article-title":"A comprehensive survey of digital image steganography and steganalysis","volume":"13","author":"Luo","year":"2024","journal-title":"APSIPA Trans Signal Inf Process"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"127528","DOI":"10.1016\/j.neucom.2024.127528","article-title":"Deep learning for steganalysis of diverse data types: a review of methods, taxonomy, challenges and future directions","volume":"581","author":"Kheddar","year":"2024","journal-title":"Neurocomputing"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"66","DOI":"10.3390\/sym17010066","article-title":"Universal image vaccine against steganography","volume":"17","author":"Wei","year":"2025","journal-title":"Symmetry"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"117","DOI":"10.3390\/sym14010117","article-title":"Comprehensive survey of multimedia steganalysis: techniques, evaluations, and trends in future research","volume":"14","author":"Shehab","year":"2022","journal-title":"Symmetry"},{"key":"ref5","first-page":"310","article-title":"Steganalysis of JPEG images: breaking the F5 algorithm","volume":"2578","author":"Fridrich","year":"2002","journal-title":"Int Workshop Inf Hiding"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1117\/12.476813","article-title":"Steganalysis of additive-noise modelable information hiding","volume":"5020","author":"Harmsen","year":"2003","journal-title":"Secur Watermarking Multimed Contents"},{"key":"ref7","series-title":"Proceedings of the 2009 International Conference on Digital Image Processing","article-title":"Feature-based steganalysis for JPEG images","author":"Li","year":"2009 Mar 7\u20139"},{"key":"ref8","series-title":"Proceedings of the the 11th ACM Workshop on Multimedia and Security","article-title":"Steganalysis by subtractive pixel adjacency matrix","author":"Pevn\u00fd","year":"2009 Sep 7\u20138"},{"key":"ref9","series-title":"Proceedings of the 2014 IEEE International Workshop on Information Forensics and Security (WIFS)","article-title":"Selection-channel-aware rich model for steganalysis of digital images","author":"Denemark","year":"2014 Dec 3\u20135"},{"key":"ref10","doi-asserted-by":"crossref","first-page":"1954","DOI":"10.1109\/TCSVT.2023.3295364","article-title":"Steganalysis feature selection with multidimensional evaluation and dynamic threshold allocation","volume":"34","author":"Ma","year":"2023","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1109\/LSP.2016.2548421","article-title":"Structural design of convolutional neural networks for steganalysis","volume":"23","author":"Xu","year":"2016","journal-title":"IEEE Signal Process Lett"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"868","DOI":"10.1109\/TIFS.2012.2190402","article-title":"Rich models for steganalysis of digital images","volume":"7","author":"Fridrich","year":"2012","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"2545","DOI":"10.1109\/TIFS.2017.2710946","article-title":"Deep learning hierarchical representations for image steganalysis","volume":"12","author":"Ye","year":"2017","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.1109\/TIFS.2018.2871749","article-title":"Deep residual network for steganalysis of digital images","volume":"14","author":"Boroumand","year":"2018","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"ref15","unstructured":"Yang J, Shi Y-Q, Wong EK, Kang X. JPEG steganalysis based on densenet. arXiv:1711.09335. 2017."},{"key":"ref16","series-title":"Proceedings of the the IEEE Conference on Computer Vision and Pattern Recognition","article-title":"Densely connected convolutional networks","author":"Huang","year":"2017 Jul 21\u201326"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1109\/TIFS.2019.2936913","article-title":"Depth-wise separable convolutions and multi-level pooling for an efficient spatial CNN-based steganalysis","volume":"15","author":"Zhang","year":"2019","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial pyramid pooling in deep convolutional networks for visual recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"1782","DOI":"10.1109\/LSP.2023.3336561","article-title":"Fast SwT-based deep steganalysis network for arbitrary-sized images","volume":"30","author":"Weng","year":"2023","journal-title":"IEEE Signal Process Lett"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"1451","DOI":"10.1109\/TCSVT.2024.3470809","article-title":"Steganalysis network with two-branch preprocessing for spatial and JPEG domains","volume":"35","author":"He","year":"2024","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"ref21","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref22","series-title":"Proceedings of the the European Conference on Computer Vision (ECCV)","article-title":"Cbam: convolutional block attention module","author":"Woo","year":"2018 Sep 8\u201314"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.1109\/LSP.2023.3313517","article-title":"Image steganalysis network based on dual-attention mechanism","volume":"30","author":"Zhang","year":"2023","journal-title":"IEEE Signal Process Lett"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"e70005","DOI":"10.1049\/ipr2.70005","article-title":"LGS-Net: a lightweight convolutional neural network based on global feature capture for spatial image steganalysis","volume":"19","author":"Ma","year":"2025","journal-title":"IET Image Process"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"25924","DOI":"10.1109\/ACCESS.2019.2900076","article-title":"Digital image steganalysis based on visual attention and deep reinforcement learning","volume":"7","author":"Hu","year":"2019","journal-title":"IEEE Access"},{"key":"ref26","first-page":"103695","article-title":"CVTStego-Net: a convolutional vision transformer architecture for spatial image steganalysis","volume":"81","author":"Bravo-Ortiz","year":"2024","journal-title":"J Inf Secur Appl"},{"key":"ref27","doi-asserted-by":"crossref","first-page":"124796","DOI":"10.1016\/j.eswa.2024.124796","article-title":"A two-stream-network based steganalysis network: TSNet","volume":"255","author":"Guo","year":"2024","journal-title":"Expert Syst Appl"},{"key":"ref28","series-title":"Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","article-title":"Yedroudj-net: an efficient CNN for spatial steganalysis","author":"Yedroudj","year":"2018 Apr 15\u201320"},{"key":"ref29","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","article-title":"Xception: deep learning with depthwise separable convolutions","author":"Chollet","year":"2017 Jul 21\u201326"},{"key":"ref30","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","article-title":"Deep residual learning for image recognition","author":"He","year":"2016 Jun 26\u201330"},{"key":"ref31","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","article-title":"Squeeze-and-excitation networks","author":"Hu","year":"2018 Jun 18\u201323"}],"container-title":["Computers, Materials & Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-85-1\/TSP_CMC_67252\/TSP_CMC_67252.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T02:01:34Z","timestamp":1763344894000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v85n1\/63561"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":31,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2025.067252","relation":{},"ISSN":["1546-2226"],"issn-type":[{"value":"1546-2226","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}