ENHANCING STEGANOGRAPHY OF GENERATIVE IMAGE BASED ON IMAGE RETOUCHING

Steganography, which hides messages within innocent-looking carriers, is an essential technique to protect data privacy. The rapid advancement of generative models makes AI-generated images a potential steganographic carrier. However, the distortion resulting from the embedding of messages makes it difficult for steganographic images to escape deep-learning based detection, especially since such distortion is more pronounced in generative images. To improve the imperceptibility of steganographic generative images, in this paper we propose a cover-source switching based steganographic scheme employing image retouching. Specifically, we utilize an image-adaptive LUTs (LookUp Tables) model to generate the LUT required to retouch the cover image. The generated LUT is then applied to the stego image, effectively obfuscating the steganographic behavior. To ensure accurate extraction, we introduce wet cost to mark ambiguous elements that are strictly prohibited from modification. Experimental results show that our scheme can significantly improve the imperceptibility of the steganographic generative images. Leveraging the reproducibility of generative images, we are able to embed secrets at the pixel level, resulting in higher payload and extraction accuracy compared to existing cover-source switching based steganographic methods.