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 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 with 8,662 samples, DeepStego demonstrates significantly lower detection rates (0.635-0.655) compared to existing methods (0.838-0.911) across multiple state-of-the-art steganalysis techniques. DeepStego supports embedding capacities up to 4 bits per word while maintaining strong detection resistance and semantic coherence. The system shows superior scalability with a factor of 1.29, compared to 1.66-1.73 for existing methods. Our evaluation demonstrates 100% message recovery accuracy and significant improvements in text quality preservation, with readability scores of 25.46 versus 22.34-24.56 for 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.