{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T15:03:23Z","timestamp":1771427003954,"version":"3.50.1"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T00:00:00Z","timestamp":1769472000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T00:00:00Z","timestamp":1771372800000},"content-version":"vor","delay-in-days":22,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cloud Comp"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The rapid growth of large language models introduces new security risks through steganographic text that closely mimics natural language. To address this challenge in cloud\u2013edge AI ecosystems, we propose WSMS (Weakly Supervised Multi-feature Steganalysis), which integrates statistical, structural, semantic, and logical features via adaptive fusion. WSMS employs weakly supervised self-training with EVT-based threshold calibration, confidence-weighted pseudo-labeling, and teacher\u2013student consistency. Experiments across multiple datasets show that WSMS effectively detects LLM-generated steganographic content and generalizes well under weak supervision. Notably, WSMS achieves 2% higher accuracy in few-shot scenarios and 8% higher accuracy under imbalanced conditions compared with baselines, demonstrating its scalability and reliability for cloud\u2013edge collaborative AI security.<\/jats:p>","DOI":"10.1186\/s13677-025-00838-6","type":"journal-article","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T05:35:07Z","timestamp":1769492107000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["WSMS: weakly supervised multi-feature steganalysis with EVT calibration for cloud-edge collaborative intelligence"],"prefix":"10.1186","volume":"15","author":[{"given":"Yingquan","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qianmu","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenhao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"He","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,27]]},"reference":[{"key":"838_CR1","doi-asserted-by":"publisher","unstructured":"Ding J, Chen K, Wang Y, Zhao N, Zhang W, Yu N (2023) Discop: provably secure steganography in practice based on \u201cDistribution Copies\u201d. In: 2023 IEEE Symposium on Security and Privacy (SP), San Francisco, CA, USA, pp 2238\u20132255. https:\/\/doi.org\/10.1109\/SP46215.2023.10179287","DOI":"10.1109\/SP46215.2023.10179287"},{"issue":"5","key":"838_CR2","doi-asserted-by":"publisher","first-page":"1280","DOI":"10.1109\/TIFS.2018.2871746","volume":"14","author":"Z-L Yang","year":"2019","unstructured":"Yang Z-L, Guo X-Q, Chen Z-M, Huang Y-F, Zhang Y-J (2019) RNN-Stega: linguistic steganography based on recurrent neural networks. IEEE Trans Inf Forensics Secur 14(5):1280\u20131295. https:\/\/doi.org\/10.1109\/TIFS.2018.2871746","journal-title":"IEEE Trans Inf Forensics Secur"},{"issue":"6","key":"838_CR3","doi-asserted-by":"publisher","first-page":"1418","DOI":"10.26599\/BDMA.2025.9020032","volume":"8","author":"J Liu","year":"2025","unstructured":"Liu J, Zhang L, Cao C, Shi Y, Mu C, Li J (2025) Zero-shot knowledge-based visual question answering with frozen language models. Big Data Min Analytics 8(6):1418\u20131431. https:\/\/doi.org\/10.26599\/BDMA.2025.9020032","journal-title":"Big Data Min Analytics"},{"issue":"3","key":"838_CR4","doi-asserted-by":"publisher","first-page":"726","DOI":"10.26599\/BDMA.2024.9020098","volume":"8","author":"W Cao","year":"2025","unstructured":"Cao W, Yao X, Xu Z, Liu Y, Pan Y, Ming Z (2025) A survey of zero-shot object detection. Big Data Min Analytics 8(3):726\u2013750. https:\/\/doi.org\/10.26599\/BDMA.2024.9020098","journal-title":"Big Data Min Analytics"},{"issue":"3","key":"838_CR5","doi-asserted-by":"publisher","first-page":"624","DOI":"10.26599\/BDMA.2024.9020092","volume":"8","author":"K Taha","year":"2025","unstructured":"Taha K, Yoo PD, Yeun C, Taha A (2025) Text classification techniques: a holistic review, observational analysis, and experimental investigation. Big Data Min Analytics 8(3):624\u2013660. https:\/\/doi.org\/10.26599\/BDMA.2024.9020092","journal-title":"Big Data Min Analytics"},{"issue":"5","key":"838_CR6","doi-asserted-by":"publisher","first-page":"1075","DOI":"10.26599\/BDMA.2025.9020012","volume":"8","author":"B Dong","year":"2025","unstructured":"Dong B, Wang W, Sun L, Liu X, Mai Z (2025) Anomaly detection using graph anomaly rules. Big Data Min Analytics 8(5):1075\u20131091. https:\/\/doi.org\/10.26599\/BDMA.2025.9020012","journal-title":"Big Data Min Analytics"},{"key":"838_CR7","unstructured":"Mathew Y, Matthews O, McCarthy R, Velja J, Witt C, Cope D, Schoots N (2024) Hidden in plain text: emergence & mitigation of steganographic collusion in llms. arXiv preprint arXiv:2410.03768. https:\/\/arxiv.org\/abs\/2410.03768 [cs.CL]"},{"key":"838_CR8","doi-asserted-by":"publisher","unstructured":"Bai M, Yang J, Pang K, Wang H, Huang Y (2024). Towards next-generation steganalysis: llms unleash the power of detecting steganography. arXiv preprint arXiv:2405.09090. https:\/\/doi.org\/10.48550\/arXiv.2405.09090","DOI":"10.48550\/arXiv.2405.09090"},{"key":"838_CR9","doi-asserted-by":"publisher","unstructured":"Liu Y, Zhou X, Kou H, Zhao Y, Xu X, Zhang X, Qi L (2024) Privacy-preserving point-of-interest recommendation based on simplified graph convolutional network for geological traveling. ACM Trans Intell Syst Technol 15(4). https:\/\/doi.org\/10.1145\/3620677","DOI":"10.1145\/3620677"},{"issue":"2","key":"838_CR10","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1109\/TSUSC.2017.2733018","volume":"4","author":"F Fei","year":"2019","unstructured":"Fei F, Li S, Dai H, Hu C, Dou W, Ni Q (2019) A k-anonymity based schema for location privacy preservation. IEEE Trans Sustain Comput 4(2):156\u2013167. https:\/\/doi.org\/10.1109\/TSUSC.2017.2733018","journal-title":"IEEE Trans Sustain Comput"},{"key":"838_CR11","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1016\/j.ins.2018.11.030","volume":"480","author":"L Qi","year":"2019","unstructured":"Qi L, Wang R, Hu C, Li S, He Q, Xu X (2019) Time-aware distributed service recommendation with privacy-preservation. Inf Sci 480:354\u2013364. https:\/\/doi.org\/10.1016\/j.ins.2018.11.030","journal-title":"Inf Sci"},{"key":"838_CR12","doi-asserted-by":"publisher","first-page":"576","DOI":"10.1016\/j.ins.2018.12.051","volume":"527","author":"W Dou","year":"2020","unstructured":"Dou W, Tang W, Wu X, Qi L, Xu X, Zhang X, Hu C (2020) An insurance theory based optimal cyber-insurance contract against moral hazard. Inf Sci 527:576\u2013589","journal-title":"Inf Sci"},{"issue":"1","key":"838_CR13","doi-asserted-by":"publisher","first-page":"18","DOI":"10.26599\/TST.2023.9010158","volume":"30","author":"X Wang","year":"2025","unstructured":"Wang X, Ma J (2025) Cloud-network-end collaborative security for wireless networks: architecture, mechanisms, and applications. Tsinghua Sci Technol 30(1):18\u201333. https:\/\/doi.org\/10.26599\/TST.2023.9010158","journal-title":"Tsinghua Sci Technol"},{"issue":"2","key":"838_CR14","doi-asserted-by":"publisher","first-page":"809","DOI":"10.26599\/TST.2024.9010198","volume":"31","author":"Z Zhang","year":"2026","unstructured":"Zhang Z, Zhuge J, Zhou X (2026) Vplocator: vulnerability patch localization model based on llm. Tsinghua Sci Technol 31(2):809\u2013822. https:\/\/doi.org\/10.26599\/TST.2024.9010198","journal-title":"Tsinghua Sci Technol"},{"issue":"2","key":"838_CR15","doi-asserted-by":"publisher","first-page":"851","DOI":"10.26599\/TST.2024.9010251","volume":"31","author":"Y Mao","year":"2026","unstructured":"Mao Y, Rong Y, Chi F, Li G, Xu H, Ping P (2026) Personalized federated learning over edge-cloud collaborative network for intelligent sensing analysis. Tsinghua Sci Technol 31(2):851\u2013866. https:\/\/doi.org\/10.26599\/TST.2024.9010251","journal-title":"Tsinghua Sci Technol"},{"issue":"2","key":"838_CR16","doi-asserted-by":"publisher","first-page":"1198","DOI":"10.26599\/TST.2025.9010023","volume":"31","author":"Z Zhu","year":"2026","unstructured":"Zhu Z, Zhang Z, Chen K, Tang D, Cai Q (2026) Resource optimisation method for multi-agent manufacturing system based on cloud-edge collaboration architecture. Tsinghua Sci Technol 31(2):1198\u20131215. https:\/\/doi.org\/10.26599\/TST.2025.9010023","journal-title":"Tsinghua Sci Technol"},{"issue":"3","key":"838_CR17","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1109\/TBDATA.2016.2602849","volume":"4","author":"L Qi","year":"2018","unstructured":"Qi L, Xu X, Zhang X, Dou W, Hu C, Zhou Y, Yu J (2018) Structural balance theory-based e-commerce recommendation over big rating data. IEEE Trans Big Data 4(3):301\u2013312. https:\/\/doi.org\/10.1109\/TBDATA.2016.2602849","journal-title":"IEEE Trans Big Data"},{"key":"838_CR18","doi-asserted-by":"publisher","unstructured":"Wu T, Dou W, Wu F, Tang S, Hu C, Chen J (2016) A deployment optimization scheme over multimedia big data for large-scale media streaming application. ACM Trans Multimedia Comput Commun Appl 12(5s). https:\/\/doi.org\/10.1145\/2983642","DOI":"10.1145\/2983642"},{"key":"838_CR19","doi-asserted-by":"publisher","unstructured":"Wang F, Qi L, Liu W, Yu B, Chen J, Xu Y (2025) Inter- and intra-similarity preserved counterfactual incentive effect estimation for recommendation systems. ACM Trans Inf Syst 43(6). https:\/\/doi.org\/10.1145\/3722104","DOI":"10.1145\/3722104"},{"key":"838_CR20","doi-asserted-by":"publisher","unstructured":"Samanta S, Dutta S, Sanyal G (2016) A real time text steganalysis by using statistical method. In: 2016 IEEE International Conference on Engineering and Technology (ICETECH), IEEE, Coimbatore, India, pp 264\u2013268. https:\/\/doi.org\/10.1109\/ICETECH.2016.7569256","DOI":"10.1109\/ICETECH.2016.7569256"},{"issue":"4","key":"838_CR21","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1109\/LSP.2019.2902095","volume":"26","author":"Z Yang","year":"2019","unstructured":"Yang Z, Huang Y, Zhang Y-J (2019) A fast and efficient text steganalysis method. IEEE Signal Process Lett 26(4):627\u2013631. https:\/\/doi.org\/10.1109\/LSP.2019.2902095","journal-title":"IEEE Signal Process Lett"},{"issue":"3","key":"838_CR22","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1109\/LSP.2019.2895286","volume":"26","author":"J Wen","year":"2019","unstructured":"Wen J, Zhou X, Zhong P, Xue Y (2019) Convolutional neural network based text steganalysis. IEEE Signal Process Lett 26(3):460\u2013464. https:\/\/doi.org\/10.1109\/LSP.2019.2895286","journal-title":"IEEE Signal Process Lett"},{"issue":"12","key":"838_CR23","doi-asserted-by":"publisher","first-page":"1743","DOI":"10.1109\/LSP.2019.2920452","volume":"26","author":"Z Yang","year":"2019","unstructured":"Yang Z, Wang K, Li J, Huang Y, Zhang Y-J (2019) Ts-rnn: text steganalysis based on recurrent neural networks. IEEE Signal Process Lett 26(12):1743\u20131747. https:\/\/doi.org\/10.1109\/LSP.2019.2920452","journal-title":"IEEE Signal Process Lett"},{"issue":"12","key":"838_CR24","doi-asserted-by":"publisher","first-page":"1907","DOI":"10.1109\/LSP.2019.2953953","volume":"26","author":"Y Niu","year":"2019","unstructured":"Niu Y, Wen J, Zhong P, Xue Y (2019) A hybrid r-bilstm-c neural network based text steganalysis. IEEE Signal Process Lett 26(12):1907\u20131911. https:\/\/doi.org\/10.1109\/LSP.2019.2953953","journal-title":"IEEE Signal Process Lett"},{"issue":"2","key":"838_CR25","doi-asserted-by":"publisher","first-page":"1476","DOI":"10.1109\/TDSC.2022.3156972","volume":"20","author":"S Li","year":"2023","unstructured":"Li S, Wang J, Liu P (2023) Detection of generative linguistic steganography based on explicit and latent text word relation mining using deep learning. IEEE Trans Depend Secure Comput 20(2):1476\u20131487. https:\/\/doi.org\/10.1109\/TDSC.2022.3156972","journal-title":"IEEE Trans Depend Secure Comput"},{"key":"838_CR26","doi-asserted-by":"publisher","first-page":"1371","DOI":"10.1109\/LSP.2025.3538494","volume":"32","author":"Z Yang","year":"2025","unstructured":"Yang Z, Xu Z, Xu X, Yang Z, Zhang R (2025) Linguistic steganalysis via text dual attention fusing statistical and multi-layer semantic features. IEEE Signal Process Lett 32:1371\u20131375. https:\/\/doi.org\/10.1109\/LSP.2025.3538494","journal-title":"IEEE Signal Process Lett"},{"issue":"6","key":"838_CR27","doi-asserted-by":"publisher","first-page":"4554","DOI":"10.1109\/TNET.2024.3402561","volume":"32","author":"R Gu","year":"2024","unstructured":"Gu R, Wang S, Dai H, Chen X, Wang Z, Bao W, Zheng J, Tu Y, Huang Y, Qi L, Xu X, Dou W, Chen G (2024) Fluid-shuttle: efficient cloud data transmission based on serverless computing compression. IEEE\/ACM Trans Netw 32(6):4554\u20134569. https:\/\/doi.org\/10.1109\/TNET.2024.3402561","journal-title":"IEEE\/ACM Trans Netw"},{"issue":"10","key":"838_CR28","doi-asserted-by":"publisher","first-page":"3161","DOI":"10.1109\/JSAC.2023.3310077","volume":"41","author":"L Qi","year":"2023","unstructured":"Qi L, Xu X, Wu X, Ni Q, Yuan Y, Zhang X (2023) Digital-twin-enabled 6g mobile network video streaming using mobile crowdsourcing. IEEE J Sel Areas Commun 41(10):3161\u20133174. https:\/\/doi.org\/10.1109\/JSAC.2023.3310077","journal-title":"IEEE J Sel Areas Commun"},{"issue":"6","key":"838_CR29","doi-asserted-by":"publisher","first-page":"5444","DOI":"10.1109\/TKDE.2022.3168611","volume":"35","author":"L Qi","year":"2023","unstructured":"Qi L, Lin W, Zhang X, Dou W, Xu X, Chen J (2023) A correlation graph based approach for personalized and compatible web apis recommendation in mobile app development. IEEE Trans Knowl Data Eng 35(6):5444\u20135457. https:\/\/doi.org\/10.1109\/TKDE.2022.3168611","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"2","key":"838_CR30","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1109\/TCSS.2022.3168595","volume":"10","author":"S Wu","year":"2023","unstructured":"Wu S, Shen S, Xu X, Chen Y, Zhou X, Liu D, Xue X, Qi L (2023) Popularity-aware and diverse web apis recommendation based on correlation graph. IEEE Trans Comput Soc Syst 10(2):771\u2013782. https:\/\/doi.org\/10.1109\/TCSS.2022.3168595","journal-title":"IEEE Trans Comput Soc Syst"},{"key":"838_CR31","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1117\/12.649551","volume-title":"Security, steganography, and watermarking of multimedia contents viii","author":"CM Taskiran","year":"2006","unstructured":"Taskiran CM, Topkara U, Topkara M, Delp EJ (2006) Attacks on lexical natural language steganography systems. In: Delp EJ, Wong PW (eds) Security, steganography, and watermarking of multimedia contents VIII, vol 6072. SPIE, San Jose, CA, pp 97\u2013105. https:\/\/doi.org\/10.1117\/12.649551"},{"key":"838_CR32","doi-asserted-by":"publisher","unstructured":"Sui X-G, Luo H, Zhu Z-L (2006) A steganalysis method based on the distribution of first letters of words. In: 2006 International Conference on Intelligent Information Hiding and Multimedia, IEEE, Washington, DC, pp 369\u2013372. https:\/\/doi.org\/10.1109\/IIH-MSP.2006.265019","DOI":"10.1109\/IIH-MSP.2006.265019"},{"key":"838_CR33","doi-asserted-by":"publisher","unstructured":"Li L, Huang L, Zhao X, Yang W, Chen Z (2008) A statistical attack on a kind of word-shift text-steganography. In: 2008 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IEEE, Washington, DC, pp 1503\u20131507. https:\/\/doi.org\/10.1109\/IIH-MSP.2008.42","DOI":"10.1109\/IIH-MSP.2008.42"},{"key":"838_CR34","doi-asserted-by":"publisher","unstructured":"Chen Z-L, Huang L-S, Yu Z-S, Li L-J, Yang W (2008) A statistical algorithm for linguistic steganography detection based on distribution of words. In: 2008 Third International Conference on Availability, Reliability and Security, IEEE, Washington, DC, pp 558\u2013563. https:\/\/doi.org\/10.1109\/ARES.2008.61","DOI":"10.1109\/ARES.2008.61"},{"key":"838_CR35","doi-asserted-by":"publisher","unstructured":"Zhao X-L, Chen Z-L, Huang L-S, Yu Z-S (2008) Effective linguistic steganography detection. In: 2008 IEEE 8th International Conference on Computer and Information Technology Workshops, IEEE, Washington, DC, pp 224\u2013229. https:\/\/doi.org\/10.1109\/CIT.2008.Workshops.69","DOI":"10.1109\/CIT.2008.Workshops.69"},{"key":"838_CR36","doi-asserted-by":"publisher","unstructured":"Meng P, Huang L, Yang W, Chen Z, Zheng H (2009) Linguistic steganography detection algorithm using statistical language model. In: 2009 International Conference on Information Technology and Computer Science, vol 2. IEEE, Washington, DC, pp 540\u2013543. https:\/\/doi.org\/10.1109\/ITCS.2009.246","DOI":"10.1109\/ITCS.2009.246"},{"key":"838_CR37","doi-asserted-by":"publisher","first-page":"1893","DOI":"10.1007\/s11042-012-1313-8","volume":"71","author":"L Xiang","year":"2014","unstructured":"Xiang L, Sun X, Luo G et al (2014) Linguistic steganalysis using the features derived from synonym frequency. Multimed Tools Appl 71:1893\u20131911. https:\/\/doi.org\/10.1007\/s11042-012-1313-8","journal-title":"Multimed Tools Appl"},{"key":"838_CR38","doi-asserted-by":"publisher","unstructured":"Yang H, Bao Y, Yang Z, Liu S, Huang Y, Jiao S (2020) Linguistic steganalysis via densely connected LSTM with feature pyramid. In: Proceedings of the 2020 ACM Workshop on Information Hiding and Multimedia Security, ACM, Denver, CO, USA, pp 5\u201310. https:\/\/doi.org\/10.1145\/3369412.3395067","DOI":"10.1145\/3369412.3395067"},{"key":"838_CR39","doi-asserted-by":"publisher","first-page":"1510","DOI":"10.1109\/LSP.2021.3097241","volume":"28","author":"W Peng","year":"2021","unstructured":"Peng W, Zhang J, Xue Y, Yang Z (2021) Real-time text steganalysis based on multi-stage transfer learning. IEEE Signal Process Lett 28:1510\u20131514. https:\/\/doi.org\/10.1109\/LSP.2021.3097241","journal-title":"IEEE Signal Process Lett"},{"key":"838_CR40","doi-asserted-by":"publisher","first-page":"558","DOI":"10.1109\/LSP.2021.3062233","volume":"28","author":"H Wu","year":"2021","unstructured":"Wu H, Yi B, Ding F, Feng G, Zhang X (2021) Linguistic steganalysis with graph neural networks. IEEE Signal Process Lett 28:558\u2013562. https:\/\/doi.org\/10.1109\/LSP.2021.3062233","journal-title":"IEEE Signal Process Lett"},{"key":"838_CR41","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1109\/LSP.2021.3122901","volume":"29","author":"J Yang","year":"2022","unstructured":"Yang J, Yang Z, Zhang S, Tu H, Huang Y (2022) SeSy: linguistic steganalysis framework integrating semantic and syntactic features. IEEE Signal Process Lett 29:31\u201335. https:\/\/doi.org\/10.1109\/LSP.2021.3122901","journal-title":"IEEE Signal Process Lett"},{"key":"838_CR42","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.ins.2021.11.086","volume":"586","author":"Y Xue","year":"2022","unstructured":"Xue Y, Kong L, Peng W, Zhong P, Wen J (2022) An effective linguistic steganalysis framework based on hierarchical mutual learning. Inf Sci 586:140\u2013154. https:\/\/doi.org\/10.1016\/j.ins.2021.11.086","journal-title":"Inf Sci"},{"key":"838_CR43","doi-asserted-by":"publisher","first-page":"3513","DOI":"10.1109\/TASLP.2023.3319975","volume":"31","author":"W Peng","year":"2023","unstructured":"Peng W, Li S, Qian Z, Zhang X (2023) Text steganalysis based on hierarchical supervised learning and dual attention mechanism. IEEE\/ACM Trans Audio Speech Lang Process. 31:3513\u20133526. https:\/\/doi.org\/10.1109\/TASLP.2023.3319975","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process."},{"key":"838_CR44","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1007\/978-981-99-8073-4_9","volume-title":"Neural information processing. ICONIP 2023. Lecture notes in computer science","author":"K Pang","year":"2024","unstructured":"Pang K et al (2024) Cats: connection-aware and interaction-based text steganalysis in social networks. In: Luo B, Cheng L, Wu ZG, Li H, Li C (eds) Neural information processing. ICONIP 2023. Lecture notes in computer science, vol 14451. Springer, Singapore, pp 120\u2013132. https:\/\/doi.org\/10.1007\/978-981-99-8073-4_9"},{"key":"838_CR45","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/978-3-031-82907-9_9","volume-title":"Computational and experimental simulations in engineering. ICCES 2024. Mechanisms and machine science","author":"Q Niu","year":"2025","unstructured":"Niu Q, Yang Z (2025) Text steganalysis with language style for social networks. In: Zhou K (ed) Computational and experimental simulations in engineering. ICCES 2024. Mechanisms and machine science, vol 176. Springer, Cham, pp 115\u2013126. https:\/\/doi.org\/10.1007\/978-3-031-82907-9_9"},{"key":"838_CR46","doi-asserted-by":"publisher","first-page":"4870","DOI":"10.1109\/TIFS.2023.3298210","volume":"18","author":"H Wang","year":"2023","unstructured":"Wang H, Yang Z, Yang J, Chen C, Huang Y (2023) Linguistic steganalysis in few-shot scenario. IEEE Trans Inf Forensics Secur 18:4870\u20134882. https:\/\/doi.org\/10.1109\/TIFS.2023.3298210","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"838_CR47","doi-asserted-by":"publisher","first-page":"128260","DOI":"10.1016\/j.neucom.2024.128260","volume":"603","author":"H You","year":"2024","unstructured":"You H, Xiang L, Yang C, Shen X (2024) Linguistic steganalysis via multi-task with crossing generative-natural domain. Neurocomputing 603:128260. https:\/\/doi.org\/10.1016\/j.neucom.2024.128260","journal-title":"Neurocomputing"},{"key":"838_CR48","doi-asserted-by":"publisher","first-page":"123437","DOI":"10.1016\/j.eswa.2024.123437","volume":"249","author":"K Yuan","year":"2024","unstructured":"Yuan K, Yang Y, Zhang Z, Wen J (2024) Multi-task few-shot text steganalysis based on context-attentive prototypes. Expert Syst Appl 249:123437. https:\/\/doi.org\/10.1016\/j.eswa.2024.123437","journal-title":"Expert Syst Appl"},{"key":"838_CR49","doi-asserted-by":"publisher","first-page":"41486","DOI":"10.1109\/ACCESS.2025.3547813","volume":"13","author":"VAK Tomita","year":"2025","unstructured":"Tomita VAK, Marcacini RM (2025) Pseudo-labeling domain adaptation using multi-model learning. IEEE Access 13:41486\u201341504. https:\/\/doi.org\/10.1109\/ACCESS.2025.3547813","journal-title":"IEEE Access"},{"issue":"4","key":"838_CR50","doi-asserted-by":"publisher","first-page":"3514","DOI":"10.1109\/TDSC.2025.3532524","volume":"22","author":"Z Zhang","year":"2025","unstructured":"Zhang Z, Wen J, Gao L, Peng W, Xue Y (2025) Linguistic steganalysis based on few-shot adversarial training. IEEE Trans Dependable Secure Comput 22(4):3514\u20133528. https:\/\/doi.org\/10.1109\/TDSC.2025.3532524","journal-title":"IEEE Trans Dependable Secure Comput"},{"key":"838_CR51","doi-asserted-by":"publisher","unstructured":"Huang K, Zhang Z, Wei Y, Zhang T, Yang Z, Zhou L (2025). Gsdfuse: capturing cognitive inconsistencies from multi-dimensional weak signals in social media steganalysis. arXiv preprint arXiv:2505.17085. https:\/\/doi.org\/10.48550\/arXiv.2505.17085","DOI":"10.48550\/arXiv.2505.17085"},{"key":"838_CR52","doi-asserted-by":"publisher","unstructured":"Gehrmann S, Strobelt H, Rush AM (2019) GLTR: statistical detection and visualization of generated text. In: Costa-Juss\u00e0 MR, Alfonseca E (eds) Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, Association for Computational Linguistics, Florence, Italy, pp 111\u2013116. https:\/\/doi.org\/10.18653\/v1\/P19-3019","DOI":"10.18653\/v1\/P19-3019"},{"key":"838_CR53","unstructured":"Zellers R, Holtzman A, Rashkin H, Bisk Y, Farhadi A, Roesner F, Choi Y (2019) Defending against neural fake News. In: Advances in Neural Information Processing Systems, vol 32. Curran Associates, Inc, Red Hook, NY, USA, pp 9054\u20139065. https:\/\/proceedings.neurips.cc\/paper\/2019\/file\/3e9f0fc9b2f89e043bc6233994dfcf76-Paper.pdf"},{"key":"838_CR54","unstructured":"Mitchell E, Lee Y, Khazatsky A, Manning CD, Finn C (2023) DetectGPT: zero-shot Machine-generated text detection using probability curvature. In: Krause A, Brunskill E, Cho K, Engelhardt B, Sabato S, Scarlett J (eds) Proceedings of the 40th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol 202. PMLR, Honolulu, Hawaii, pp 24950\u201324962. https:\/\/proceedings.mlr.press\/v202\/mitchell23a.html"},{"key":"838_CR55","unstructured":"Wouters B (2024) Optimizing watermarks for large language models. In: Salakhutdinov R, Kolter Z, Heller K, Weller A, Oliver N, Scarlett J, Berkenkamp F (eds) Proceedings of the 41st International Conference on Machine Learning. Proceedings of Machine Learning Research, vol 235. PMLR, Vienna, Austria, pp 53251\u201353269. https:\/\/proceedings.mlr.press\/v235\/wouters24a.html"},{"key":"838_CR56","doi-asserted-by":"publisher","unstructured":"Hu Z, Chen L, Wu X, Wu Y, Zhang H, Huang H (2023) Unbiased watermark for large language models. arXiv preprint arXiv:2310.10669. https:\/\/doi.org\/10.48550\/arXiv.2310.10669","DOI":"10.48550\/arXiv.2310.10669"},{"key":"838_CR57","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1007\/978-1-4471-3675-0","volume-title":"Springer series in statistics","author":"S Coles","year":"2001","unstructured":"Coles S (2001) An introduction to statistical modeling of extreme values. In: Springer series in statistics. Springer, London, UK, p 209. https:\/\/doi.org\/10.1007\/978-1-4471-3675-0"},{"key":"838_CR58","first-page":"529","volume-title":"Advances in neural information processing systems","author":"Y Grandvalet","year":"2004","unstructured":"Grandvalet Y, Bengio Y (2004) Semi-supervised learning by entropy minimization. In: Saul L, Weiss Y, Bottou L (eds) Advances in neural information processing systems, vol 17. MIT Press, Cambridge, MA, USA, pp 529\u2013536"},{"key":"838_CR59","doi-asserted-by":"publisher","unstructured":"Xie Q, Luong M-T, Hovy E, Le QV (2020) Self-training with noisy student improves imagenet classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Seattle, WA, USA, pp 10684\u201310695. https:\/\/doi.org\/10.1109\/CVPR42600.2020.01070","DOI":"10.1109\/CVPR42600.2020.01070"}],"container-title":["Journal of Cloud Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13677-025-00838-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-025-00838-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-025-00838-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T14:04:34Z","timestamp":1771423474000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s13677-025-00838-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,27]]},"references-count":59,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["838"],"URL":"https:\/\/doi.org\/10.1186\/s13677-025-00838-6","relation":{},"ISSN":["2192-113X"],"issn-type":[{"value":"2192-113X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,27]]},"assertion":[{"value":"15 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Ethics approval was not required for this study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"19"}}