A novel gradient-guided post-processing method for adaptive image steganography.

Xie, Guoliang; Ren, Jinchang; Marshall, Stephen; Zhao, Huimin; Li, Rui

Abstract

Designing an effective cost function has always been the key in image steganography after the development of the near-optimal encoders. To learn the cost maps automatically, the Generative Adversarial Networks (GAN) are often trained from the given cover images. However, this needs to train two Convolutional Neural Networks (CNN) in theory and is thus very time-consuming. In this paper, without modifying the original stego image and the associated cost function of the steganography, and no need to train a GAN, we proposed a novel post-processing method for adaptive image steganography. The post-processing method aims at the embedding cost, hence it is called Post-cost-optimization in this paper. Given a cover image, its gradient map is learned from a pre-trained CNN, which is further smoothed by a low-pass filter. The elements of the cost map derived from the original steganography are projected to 0,1 for separating embeddable and non-embeddable areas. For embeddable areas, the elements will be further screened by the gradient map, according to the magnitudes of the gradients, to produce a new cost map. Finally, the new cost map is used to generate new stego images. Comprehensive experiments have validated the efficacy of the proposed method, which has outperformed several state-of-the-art approaches, whilst the computational cost is also significantly reduced.

Citation

XIE, G., REN, J., MARSHALL, S., ZHAO, H. and LI, R. 2023. A novel gradient-guided post-processing method for adaptive image steganography. Signal processing [online], 203, article 108813. Available from: https://doi.org/10.1016/j.sigpro.2022.108813

• Journal Article Type: Article
• Acceptance Date: Oct 16, 2022
• Online Publication Date: Oct 23, 2022
• Publication Date: Feb 28, 2023
• Deposit Date: Oct 21, 2022
• Publicly Available Date: Oct 24, 2023
• Journal: Signal processing
• Print ISSN: 0165-1684
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 203
• Article Number: 108813
• DOI: https://doi.org/10.1016/j.sigpro.2022.108813
• Keywords: Neural networks; Image steganography; Image steganalysis; Gradients
• Public URL: https://rgu-repository.worktribe.com/output/1784666