基于可逆生成对抗网络的鲁棒图像隐藏  

Robust image hiding by invertible generative adversarial network

作　　者：许天佑 高光勇 XU Tianyou;GAO Guangyong(School of Computer Science,School of Cyber Science and Engineering,Nanjing University of Information Science&Technology,Nanjing 210044,China)

机构地区：[1]南京信息工程大学计算机学院、网络空间安全学院,江苏南京210044

出　　处：《计算机工程与科学》2025年第2期288-297,共10页Computer Engineering & Science

摘　　要：图像隐藏的目的是在载体图像中隐藏秘密图像,使秘密图像对人眼来说仍然是不可察觉的,但是在需要时可以恢复秘密图像。已有的图像隐藏方法在隐藏能力和鲁棒性方面受到限制,通常容易受到网络传输中图像失真的影响。因此,提出了一个名为RIHIGAN的模型。该模型在前向和后向的过程中使用同一网络来实现图像隐藏和恢复;在可逆网络模块中,通过结合注意力机制来增强模型的图像重建能力。在可逆网络的基础上,引入了生成对抗网络;同时改良了判别器的结构,结合残差块提升其判别能力。实验结果表明,RIHIGAN在保持恢复率和不可见性的同时,有效地增强了鲁棒性。The purpose of image hiding is to hide the secret image in the cover image,so that the secret image is still imperceptible to the human eyes,but can be restored when needed.Previous image hiding methods were limited in terms of hiding ability and robustness,and they are often susceptible to distortion in transmission.So,this paper proposes a model called RIHIGAN.It uses the same network through forward and backward processes to achieve image hiding and restoration.In the invertible network module,the model s image reconstruction ability is enhanced by combining attention mechanisms.On the basis of reversible networks,the architecture of generative adversarial networks is introduced.At the same time,the structure of the discriminator has been improved by combining residual blocks to enhance its discrimination ability.The experiments results show that RIHIGAN effectively enhances robustness while maintaining recovery rate and invisibility.

关 键 词：图像隐藏 信息隐藏 深度学习 可逆网络 生成对抗网络 鲁棒性 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]