DeepStego: Privacy-Preserving Natural Language Steganography Using Large Language Models and Advanced Neural Architectures
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’s language modeling capabilities for secu...
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MDPI AG
2025-04-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/14/5/165 |
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| author | Oleksandr Kuznetsov Kyrylo Chernov Aigul Shaikhanova Kainizhamal Iklassova Dinara Kozhakhmetova |
| author_facet | Oleksandr Kuznetsov Kyrylo Chernov Aigul Shaikhanova Kainizhamal Iklassova Dinara Kozhakhmetova |
| author_sort | Oleksandr Kuznetsov |
| collection | DOAJ |
| description | 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’s 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. |
| format | Article |
| id | doaj-art-467572d5c4a54c239fd856f8d77957ce |
| institution | OA Journals |
| issn | 2073-431X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj-art-467572d5c4a54c239fd856f8d77957ce2025-08-20T02:33:42ZengMDPI AGComputers2073-431X2025-04-0114516510.3390/computers14050165DeepStego: Privacy-Preserving Natural Language Steganography Using Large Language Models and Advanced Neural ArchitecturesOleksandr Kuznetsov0Kyrylo Chernov1Aigul Shaikhanova2Kainizhamal Iklassova3Dinara Kozhakhmetova4Department of Theoretical and Applied Sciences, eCampus University, Via Isimbardi 10, 22060 Novedrate, ItalyDepartment of Intelligent Software Systems and Technologies, School of Computer Science and Artificial Intelligence, V. N. Karazin Kharkiv National University, 4 Svobody Sq., 61022 Kharkiv, UkraineDepartment of Information Security, L.N. Gumilyov Eurasian National University, Satpayev 2, Astana 010008, KazakhstanDepartment of Information and Communication Technologies, Manash Kozybayev North Kazakhstan University, Pushkin Str., 86, Petropavlovsk 150000, KazakhstanHigher School of Artificial Intelligence and Construction, Shakarim University, St. Glinka, 20A, Semey 071412, KazakhstanModern 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’s 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.https://www.mdpi.com/2073-431X/14/5/165linguistic steganographyGPT modelsnatural language processinginformation hidingtext generationsemantic embedding |
| spellingShingle | Oleksandr Kuznetsov Kyrylo Chernov Aigul Shaikhanova Kainizhamal Iklassova Dinara Kozhakhmetova DeepStego: Privacy-Preserving Natural Language Steganography Using Large Language Models and Advanced Neural Architectures Computers linguistic steganography GPT models natural language processing information hiding text generation semantic embedding |
| title | DeepStego: Privacy-Preserving Natural Language Steganography Using Large Language Models and Advanced Neural Architectures |
| title_full | DeepStego: Privacy-Preserving Natural Language Steganography Using Large Language Models and Advanced Neural Architectures |
| title_fullStr | DeepStego: Privacy-Preserving Natural Language Steganography Using Large Language Models and Advanced Neural Architectures |
| title_full_unstemmed | DeepStego: Privacy-Preserving Natural Language Steganography Using Large Language Models and Advanced Neural Architectures |
| title_short | DeepStego: Privacy-Preserving Natural Language Steganography Using Large Language Models and Advanced Neural Architectures |
| title_sort | deepstego privacy preserving natural language steganography using large language models and advanced neural architectures |
| topic | linguistic steganography GPT models natural language processing information hiding text generation semantic embedding |
| url | https://www.mdpi.com/2073-431X/14/5/165 |
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