GAN-based Symmetric Embedding Costs Adjustment for Enhancing Image Steganographic Security

Ye Miaoxin, Zhou Saixing, Weiqi Luo, Shunquan Tan, Jiwu Huang

Abstract: Designing embedding costs is pivotal in modern image steganography. Many studies have shown adjusting symmetric embedding costs to asymmetric ones can enhance steganographic security. However, most existing methods heavily depend on manually defined parameters or rules, limiting security performance improvements. To overcome this limitation, we introduce an advanced GAN-based framework that transitions symmetric costs to asymmetric ones without the need for the manual intervention seen in existing approaches, such as the detailed specification of cost modulation directions and magnitudes. In our framework, we firstly achieve symmetric costs for a cover image, which is randomly split into two sub-images, with part of the secret information embedded into one. Subsequently, we design a GAN model to adjust the embedding costs of the second sub-image to asymmetric, facilitating the secure embedding of the remaining secret information. To support our phased embedding approach, our GAN's discriminator incorporates two steganalyers with different tasks: distinguishing the generator's final output, i.e., the stego image, from both the input cover image and the partially embedded stego image, providing diverse guidance to the generator. In addition, we introduce a simple yet effective update strategy to ensure a stable training process. Comprehensive experiments demonstrate that our method significantly enhances security over existing symmetric steganography techniques, achieving state-of-the-art levels compared to other methods focused on embedding costs adjustments. Additionally, detailed ablation studies validate our approach's effectiveness.

Relevance To Conference: This work significantly contributes to the field of multimedia and multimodal processing by integrating Generative Adversarial Networks (GANs) into the realm of image steganography, particularly in the modification of embedding costs for enhancing steganography security. GANs, as powerful generative models, have already shown immense potential in various applications such as image generation, editing, inpainting, and style transformation. However, their application in image steganography, especially in the context of embedding cost modification, remains underexplored. By employing GANs to adjust embedding costs, this research enhances the security of existing image steganography techniques. This improvement is crucial for the development of more secure and efficient multimedia communication systems. Overall, the application of GANs in this innovative context not only enriches the multimedia processing landscape but also sets a foundation for future explorations into secure and efficient information embedding techniques.

Primary Subject Area: [Generation] Generative Multimedia

Secondary Subject Area: [Experience] Multimedia Applications

Submission Number: 3349