Cross-Domain Robust Image Steganography via Dual-Domain Enhancement Network

Current steganographic techniques predominantly focus on single-domain security designs, while neglecting the fact that cross-domain conversions between spatial and frequency domains may compromise embedded features, introducing detectable noise and artifacts that render stego images vulnerable to steganalyzers. This letter proposes a high-performance image steganography by dual-domain adversarial training to enhance both the security and image quality in the spatial and JPEG domains. The proposed method employs a dual-domain adversarial training strategy, integrating spatial and JPEG-domain steganalyzers to guide the generator toward producing compression-resilient stego images. In addition, a dual-objective loss function is introduced, consisting of a spatial fidelity loss to ensure visual imperceptibility and a frequency-domain consistency loss to mitigate compression-induced distortions. This design enables the model to effectively learn domain-aware embedding strategies, thereby achieving enhanced cross-domain robustness and security. Extensive experiments demonstrate that the proposed method outperforms other advanced image steganographic methods in terms of security and robustness.