{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:12:52Z","timestamp":1760058772897,"version":"build-2065373602"},"reference-count":19,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T00:00:00Z","timestamp":1745884800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Modern linguistic steganography faces the fundamental challenge of balancing embedding capacity with detection resistance, particularly against advanced AI-based steganalysis. This paper presents DeepStego, a novel steganographic system leveraging GPT-4-omni\u2019s language modeling capabilities for secure information hiding in text. Our approach combines dynamic synonym generation with semantic-aware embedding to achieve superior detection resistance while maintaining text naturalness. Through comprehensive experimentation, DeepStego demonstrates significantly lower detection rates compared to existing methods across multiple state-of-the-art steganalysis techniques. DeepStego supports higher embedding capacities while maintaining strong detection resistance and semantic coherence. The system shows superior scalability compared to existing methods. Our evaluation demonstrates perfect message recovery accuracy and significant improvements in text quality preservation compared to competing approaches. These results establish DeepStego as a significant advancement in practical steganographic applications, particularly suitable for scenarios requiring secure covert communication with high embedding capacity.<\/jats:p>","DOI":"10.3390\/computers14050165","type":"journal-article","created":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T11:00:54Z","timestamp":1745924454000},"page":"165","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DeepStego: Privacy-Preserving Natural Language Steganography Using Large Language Models and Advanced Neural Architectures"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2331-6326","authenticated-orcid":false,"given":"Oleksandr","family":"Kuznetsov","sequence":"first","affiliation":[{"name":"Department of Theoretical and Applied Sciences, eCampus University, Via Isimbardi 10, 22060 Novedrate, Italy"},{"name":"Department of Intelligent Software Systems and Technologies, School of Computer Science and Artificial Intelligence, V. N. Karazin Kharkiv National University, 4 Svobody Sq., 61022 Kharkiv, Ukraine"}]},{"given":"Kyrylo","family":"Chernov","sequence":"additional","affiliation":[{"name":"Department of Intelligent Software Systems and Technologies, School of Computer Science and Artificial Intelligence, V. N. Karazin Kharkiv National University, 4 Svobody Sq., 61022 Kharkiv, Ukraine"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6006-4813","authenticated-orcid":false,"given":"Aigul","family":"Shaikhanova","sequence":"additional","affiliation":[{"name":"Department of Information Security, L.N. Gumilyov Eurasian National University, Satpayev 2, Astana 010008, Kazakhstan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8330-4282","authenticated-orcid":false,"given":"Kainizhamal","family":"Iklassova","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Technologies, Manash Kozybayev North Kazakhstan University, Pushkin Str., 86, Petropavlovsk 150000, Kazakhstan"}]},{"given":"Dinara","family":"Kozhakhmetova","sequence":"additional","affiliation":[{"name":"Higher School of Artificial Intelligence and Construction, Shakarim University, St. Glinka, 20A, Semey 071412, Kazakhstan"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"124891","DOI":"10.1016\/j.eswa.2024.124891","article-title":"Image Privacy Protection Scheme Based on High-Quality Reconstruction DCT Compression and Nonlinear Dynamics","volume":"257","author":"Lin","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Denemark, T., Fridrich, J., and Holub, V. (2014). Further Study on the Security of S-UNIWARD, Binghamton University.","DOI":"10.1117\/12.2044803"},{"key":"ref_3","unstructured":"Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018). BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding, Association for Computational Linguistics."},{"key":"ref_4","unstructured":"(2024, April 14). GPT-4. Available online: https:\/\/openai.com\/research\/gpt-4."},{"key":"ref_5","first-page":"301609","article-title":"ChatGPT for Digital Forensic Investigation: The Good, the Bad, and the Unknown","volume":"46","author":"Scanlon","year":"2023","journal-title":"Forensic Sci. Int. Digit. Investig."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Fridrich, J. (2009). Steganography in Digital Media: Principles, Algorithms, and Applications, Cambridge University Press. Illustrated Edition.","DOI":"10.1017\/CBO9781139192903"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Cox, I., Miller, M., Bloom, J., Fridrich, J., and Kalker, T. (2007). Digital Watermarking and Steganography, Morgan Kaufmann. [2nd ed.].","DOI":"10.1016\/B978-012372585-1.50015-2"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1743","DOI":"10.1109\/LSP.2019.2920452","article-title":"TS-RNN: Text Steganalysis Based on Recurrent Neural Networks","volume":"26","author":"Yang","year":"2019","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1109\/TIFS.2020.3023279","article-title":"VAE-Stega: Linguistic Steganography Based on Variational Auto-Encoder","volume":"16","author":"Yang","year":"2021","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1109\/LSP.2020.3042413","article-title":"Linguistic Steganography: From Symbolic Space to Semantic Space","volume":"28","author":"Zhang","year":"2021","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_11","unstructured":"Ettinger, A., Gella, S., Labeau, M., Alm, C.O., Carpuat, M., and Dredze, M. (2017). Generating Steganographic Text with LSTMs. Proceedings of ACL 2017, Student Research Workshop, Association for Computational Linguistics."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1109\/LSP.2021.3058889","article-title":"Linguistic Generative Steganography With Enhanced Cognitive-Imperceptibility","volume":"28","author":"Yang","year":"2021","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1109\/LSP.2023.3302749","article-title":"A Secure and Disambiguating Approach for Generative Linguistic Steganography","volume":"30","author":"Yan","year":"2023","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_14","unstructured":"Zhao, X., Shi, Y.-Q., Piva, A., and Kim, H.J. High-Performance Linguistic Steganalysis, Capacity Estimation and Steganographic Positioning. Proceedings of the Digital Forensics and Watermarking."},{"key":"ref_15","unstructured":"Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C.L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., and Ray, A. (December, January 28). Training Language Models to Follow Instructions with Human Feedback. Proceedings of the 36th International Conference on Neural Information Processing Systems (NIPS \u201922), Orleans, LA, USA."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1907","DOI":"10.1109\/LSP.2019.2953953","article-title":"A Hybrid R-BILSTM-C Neural Network Based Text Steganalysis","volume":"26","author":"Niu","year":"2019","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1109\/LSP.2019.2895286","article-title":"Convolutional Neural Network Based Text Steganalysis","volume":"26","author":"Wen","year":"2019","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yang, H., Bao, Y., Yang, Z., Liu, S., Huang, Y., and Jiao, S. Linguistic Steganalysis via Densely Connected LSTM with Feature Pyramid. Proceedings of the 2020 ACM Workshop on Information Hiding and Multimedia Security.","DOI":"10.1145\/3369412.3395067"},{"key":"ref_19","unstructured":"Yang, Z., Wei, N., Sheng, J., Huang, Y., and Zhang, Y. (2018). TS-CNN: Text Steganalysis from Semantic Space Based on Convolutional Neural Network. arXiv."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/5\/165\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:24:14Z","timestamp":1760030654000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/5\/165"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,29]]},"references-count":19,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["computers14050165"],"URL":"https:\/\/doi.org\/10.3390\/computers14050165","relation":{},"ISSN":["2073-431X"],"issn-type":[{"type":"electronic","value":"2073-431X"}],"subject":[],"published":{"date-parts":[[2025,4,29]]}}}