###### Abstract  
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. L  
Recent advances in linguistic steganography have primarily focused on improving embedding efficiency through various text manipulation techniques. These methods often rely on predefined rules or statistical patterns for message encoding. While such approaches have achieved moderate success, they remain vulnerable to increasingly sophisticated steganalysis methods. Current techniques typically achieve detection rates between 0.838 and 0.911, making them inadequate for highly secure applications [2,3]. The emergence of advanced language models, particularly GPT architectures [4,5], presents new opportunities for enhancing steganographic techniques. These models' sophisticated understanding of language semantics and context offers potential solutions to the fundamental challenges in linguistic steganography. However, effectively leveraging these capabilities for secure information hiding requires novel approaches to text generation and message embedding. This paper introduces DeepStego, a steganographic system that harnesses GPT-4-omni's language modeling capabilities to achieve superior detection resistance and text naturalness. Our approach combines dynamic synonym generation with semantic-aware embedding techniques to overcome the limitations of existing methods. The system demonstrates significant improvements in three critical areas: detection resistance (achieving rates of 0.635-0.655), statistical preservation, and embedding capacity. To illustrate the essence of DeepStego, consider a simple example: the phrase \"The weather today is quite pleasant\" could be transformed to \"The climate today is rather nice\" to encode the binary message \"1011\". Each word substitution (weather \(\rightarrow\) climate, quite \(\rightarrow\) rather, pleasant \(\rightarrow\) nice) represents a specific bit pattern, invisible to casual readers but recoverable with the correct key. DeepStego enhances this approach by using GPT-4-omni to generate contextually appropriate synonyms that maintain natural language flow while maximizing encoding capacity. The main contributions of this work are:  
1. A novel steganographic framework leveraging GPT-4-omni for natural text generation and semantic-aware message embedding;
2. An adaptive embedding mechanism that maintains strong detection resistance while supporting high-capacity information hiding;
3. Comprehensive empirical evaluation demonstrating significant improvements in security and text quality metrics;
4. A practical implementation achieving 100% message recovery accuracy with superior scalability. The remainder of this paper is organized as follows. Section 2 reviews related work in linguistic steganography and language modeling. Section 3 presents our system architecture and methodology. Section 4 details our experimental results and performance analysis.