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.
