Dual-domain joint optimization for universal JPEG steganography

Abstract

JPEG steganography hides secret messages by modifying DCT coefficients in cover images to ensure the undetectability. Currently, most JPEG steganography methods rely on the additive distortion model, assigning adaptive costs to individual coefficients. However, these costs are often designed within a single domain, either DCT or decompressed domain, thereby making the hiding behavior detectable by multi-domain steganalysis features. To improve the steganography security, this paper proposes an universal Dual-Domain JPEG Additive Distortion (DDJAD) framework, which minimize additive distortion in both JPEG and decompressed domain concurrently. A measure to reduce the perceptibility of coefficient modifications within pixel blocks is first formulated and then incorporated into the traditional JPEG steganography model. Finally, the new additive distortion model is solved by fine-tuning existing costs, illustrating the universality of the proposed method. Experimental results showcase the superiority of the proposed method over previous works across various quality factors and payloads, employing typical steganalysis features.