A Novel Coverless Image Steganography Scheme Based on LLM-Guided Image Generation

Abstract

With the advancement of deep learning-based steganalysis and prevalence of lossy compression mechanisms in social network transmission, traditional steganography based on cover modification (such as LSB substitution) faces dual challenges of security and robustness. This study proposes a novel steganographic framework based on generative artificial intelligence and predefined semantic mapping. Unlike embedding ciphertext in pixel noise, this method utilizes a shared mapping protocol (Codebook) to transform abstract information into concrete visual elements (such as characters, actions, scenes, and styles), and constructs stego-images through generative models. Experimental results show that when both parties share the same key, the system achieves full semantic recovery. Using an explicitly specified pipeline (Gemini 2.5 Flash Image for synthesis and Gemini 2.5 Flash for parsing), we evaluate the scheme on an enlarged, randomly sampled scenario set spanning three to six active semantic dimensions. Across these scenarios, we report per-dimension accuracy, the end-to-end full-recovery rate with 95% confidence intervals, and the partial-recovery rate under controlled JPEG compression, re-scaling, Gaussian noise, and cropping, rather than a single aggregate figure. The results indicate that carrying information at the semantic level yields graceful degradation under common channel distortions together with high visual camouflage, while also revealing that recovery reliability decreases as more semantic dimensions are activated simultaneously. We therefore present these findings as a proof of concept and explicitly separate demonstrated results from hypotheses left to future work. We therefore present these findings as a proof of concept and explicitly separate demonstrated results from hypotheses left to future work.