SSHR: More Secure Generative Steganography with High-Quality Revealed Secret Images

Jiannian Wang, Yao Lu, Guangming Lu

TL;DR: We systematically propose a novel generative steganography method joints the Reference Images with the adaptive keys to govern the entire process, enhancing the naturalness of stego images, recovery quality and secret image security

Abstract: Image steganography ensures secure information transmission and storage by concealing secret messages within images. Recently, the diffusion model has been incorporated into the generative image steganography task, with text prompts being employed to guide the entire process. However, existing methods are plagued by three problems: (1) the restricted control exerted by text prompts causes generated stego images resemble the secret images and seem unnatural, raising the severe detection risk; (2) inconsistent intermediate states between Denoising Diffusion Implicit Models and its inversion, coupled with limited control of text prompts degrade the revealed secret images; (3) the descriptive text of images(i.e. text prompts) are also deployed as the keys, but this incurs significant security risks for both the keys and the secret images.To tackle these drawbacks, we systematically propose the SSHR, which joints the Reference Images with the adaptive keys to govern the entire process, enhancing the naturalness and imperceptibility of stego images. Additionally, we methodically construct an Exact Reveal Process to improve the quality of the revealed secret images. Furthermore, adaptive Reference-Secret Image Related Symmetric Keys are generated to enhance the security of both the keys and the concealed secret images. Various experiments indicate that our model outperforms existing methods in terms of recovery quality and secret image security.

Lay Summary: Image steganography ensures secure information transmission and storage by concealing secret messages within images. Recently, the diffusion model has been incorporated into the generative image steganography task, with text prompts being employed to guide the entire process. However, existing methods are plagued by three problems: (1) the restricted control exerted by text prompts causes generated stego images resemble the secret images and seem unnatural, raising the severe detection risk; (2) inconsistent intermediate states between Denoising Diffusion Implicit Models and its inversion, coupled with limited control of text prompts degrade the revealed secret images; (3) the descriptive text of images(i.e. text prompts) are also deployed as the keys, but this incurs significant security risks for both the keys and the secret images.To tackle these drawbacks, we systematically propose the SSHR, which joints the Reference Images with the adaptive keys to govern the entire process, enhancing the naturalness and imperceptibility of stego images. Additionally, we methodically construct an Exact Reveal Process to improve the quality of the revealed secret images. Furthermore, adaptive Reference-Secret Image Related Symmetric Keys are generated to enhance the security of both the keys and the concealed secret images. Various experiments indicate that our model outperforms existing methods in terms of recovery quality and secret image security.

Application-Driven Machine Learning: This submission is on Application-Driven Machine Learning.

Primary Area: Applications->Computer Vision

Keywords: image steganography; Generative stegandgraphy; Secure; Key

Submission Number: 1080