摘要
人工智能的发展为信息隐藏技术带来越来越多的挑战,提高现有隐写方法的安全性迫在眉睫.为提高图像的信息隐藏能力,提出一种基于U-Net结构的生成式多重对抗隐写算法.所提算法通过生成对抗网络与隐写分析器优化网络、隐写分析对抗网络间的多重对抗训练,构建生成式多重对抗隐写网络模型,生成适合信息隐写的载体图像,提高隐写图像抗隐写分析能力;同时,针对现有生成对抗网络只能生成随机图像,且图像质量不高的问题,设计基于U-Net结构的生成式网络模型,将参考图像的细节信息传递到生成载体图像中,可控地生成高质量目标载体图像,增强信息隐藏能力;其次,采用图像判别损失、均方误差(MSE)损失和隐写分析损失动态加权组合作为网络迭代优化总损失,保障生成式多重对抗隐写网络快速稳定收敛.实验表明,基于U-Net结构的生成式多重对抗隐写算法生成的载体图像PSNR最高可达到48.60 dB,隐写分析器对生成载体图像及其隐写图像的判别率为50.02%,所提算法能够生成适合信息嵌入的高质量载体图像,保障隐写网络快速稳定收敛,提高了图像隐写安全性,可以有效抵御当前优秀的隐写分析算法的检测.
The development of artificial intelligence brings more and more challenges to data hiding technology,and it is urgent to improve the security of existing steganography methods.In this study,a generative multiple adversarial steganography algorithm based on U-Net network structure is proposed to improve the image data hiding ability.A generative multiple adversarial steganography network(GMASN),including the generative adversarial network,the steganalyzer optimization network and the steganalysis network,is firstly constructed,and the anti steganalysis ability of the steganography image is improved through the competition of the networks in the GMASN.At the same time,aiming at the problem that the existing generative adversarial network can only generate low-quality images randomly,a generative network based on U-Net structure is designed to transfer the details of the reference image to the generated carrier image,by which the image can be generated objectively with high visual quality.Moreover,the image discrimination loss,mean square error(MSE)loss,and steganalysis loss are dynamically combined in the proposed scheme to enable the GMASN to converge rapidly and stably.Experimental results show that the PSNR of the generated carrier image can reach 48.60 dB,and the discrimination rate between the generated carrier image and the steganographic image is 50.02%.The proposed algorithm can generate high-quality carrier images suitable for data hiding,enable the steganographic network to converge rapidly and stably,and improve the security of image steganography effectively.
作者
马宾
韩作伟
徐健
王春鹏
李健
王玉立
MA Bin;HAN Zuo-Wei;XU Jian;WANG Chun-Peng;LI Jian;WANG Yu-Li(School of Cyber Security,Qilu University of Technology(Shandong Academy of Sciences),Jinan 250353,China;Shandong Provincial Key Laboratory of Computer Networks,Jinan 250098,China;School of Computer Science and Technology,Shandong University of Finance and Economics,Jinan 250014,China)
出处
《软件学报》
EI
CSCD
北大核心
2023年第7期3385-3407,共23页
Journal of Software
基金
国家自然科学基金(61802212,61872203)
山东省重大科技创新工程(2019JZZY010127,2019JZZY010132,2019JZZY010201)
山东省自然科学基金(ZR2019BF017,ZR2020MF054)
山东省高校科研计划(J18KA331)
山东省高等学校青创人才引育计划(S019-161)
济南市“高校20条”引进创新团队(2019GXRC031)。
关键词
隐写
隐写分析
生成对抗网络
多重对抗
U-Net
steganography
steganalysis
generative adversarial network(GAN)
multiple adversarial
U-Net
