基于双向生成对抗网络的图像感知哈希算法  

An Image Perceptual Hash Algorithm Based on Bidirectional Generative Adversarial Network

作　　者：马宾[1] 王一利 徐健 王春鹏 李健 周琳娜 MA Bin;WANG Yi-li;XU Jian;WANG Chun-peng;LI Jian;ZHOU Lin-na(Department of Computer Science and Technology,Qilu University of Technology(Shandong Academy of Sciences),Jinan,Shandong 250300,China;Department of Computer Science and Technology,Shandong University of Finance and Economics,Jinan,Shandong 250014,China;Department of Cyber Security,Beijing University of Posts and Telecommunications,Beijing 100876,China)

机构地区：[1]齐鲁工业大学大学(山东省科学院)计算机科学与技术学部,山东济南250300 [2]山东财经大学计算机科学与技术学院,山东济南250014 [3]北京邮电大学网络空间安全学院,北京100876

出　　处：《电子学报》2023年第5期1405-1412,共8页Acta Electronica Sinica

基　　金：国家自然科学基金(No.62272255,No.61872203);国家重点研发计划(No.2021YFC3340600);山东省自然科学基金(No.ZR2019BF017,No.ZR2020MF054);山东省自然科学基金创新发展联合基金(No.ZR202208310038)。

摘　　要：本文提出一种基于双向生成对抗网络(Bidirectional Generative Adversarial Network, BiGAN)的无监督感知哈希生成算法,通过编码网络、生成网络和判别网络间的双向迭代对抗,生成具有较强图像语义特征表示能力的感知哈希码.本算法通过在编码网络和生成网络间添加跳接层网络结构,将原始图像不同维度的特征信息传递到生成网络,提高生成图像语义学习能力与网络收敛速度;同时,在对抗损失中添加均方误差(Mean Sequare Error, MSE)损失,增强生成图像的视觉质量与细节表示能力.最后,基于网络间的多重迭代对抗训练,输出兼备相同来源图像鲁棒性和不同来源图像区分性的高性能图像感知哈希码.本研究首次采用大型图像数据库进行算法性能评价,实验结果表明,基双向生成对抗网络的感知哈希生成算法与当前其他最新研究方案相比具有更强的版权认证与来源检测能力.An unsupervised perceptual hash generation algorithm based on a bidirectional generative adversarial net-work(BiGAN)is presented.It generates perceptual hash codes with strong image semantic representation capabilities through bidirectional iterative competition between encoding networks,generation networks,and discrimination networks.Moreover,by adding a skip-connection network structure between the coding network and the generation network,different dimensional features of the original image are transformed from the coding network to the generation network,improving the semantic expression ability of the generated image and convergence speed of the network.At the same time,the mean square error(MSE)loss is added to the network adversarial losses to enhance the visual quality and detail representation ability of the generated image.Finally,a high-performance image perception hash code that possesses the robustness of the same source images and the distinguishability of different source images is obtained via multiple iterative adversarial train-ing networks.A large image database is used for the first time to evaluate the performance of perceptual hash generation schemes in this study.Extensive experimental results show that the proposed algorithm has stronger copyright authentica-tion and source detection capabilities than other state-of-the-art schemes.

关 键 词：感知哈希 生成对抗网络 均方误差 来源检测 哈希码 图像内容认证 

分 类 号：TN911.73[电子电信—通信与信息系统]