基于双向生成对抗网络的图像感知哈希算法

马宾; 王一利; 徐健; 王春鹏; 李健; 周琳娜

doi:10.12263/DZXB.20221224

您当前的位置：

首页 >

文章列表页 >

基于双向生成对抗网络的图像感知哈希算法

学术论文 | 更新时间：2025-12-08

- 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
- 电子学报 2023年51卷第5期页码：1405-1412
- 作者机构：
  
  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
- 作者简介：
  
  [ "马宾男，1973年出生，山东济宁人.博士，齐鲁工业大学（山东省科学院）教授.主要研究方向为多媒体信息安全. E-mail: sddxmb@126.com" ]
  [ "王一利男，1998年出生，山东济宁人.齐鲁工业大学（山东省科学院）研究生，主要研究方向为图像感知哈希." ]
  [ "徐　健（通讯作者）女，1973年出生，山东潍坊人.硕士，山东财经大学副教授，主要研究方向为多媒体信息安全. E-mail: sdfixj@126.com" ]
- 基金信息：
  
  国家自然科学基金(62272255;61872203);国家重点研发计划(2021YFC3340600);山东省自然科学基金(ZR2019BF017;ZR2020MF054);山东省自然科学基金创新发展联合基金(ZR202208310038)
- DOI：10.12263/DZXB.20221224
  中图分类号： TN911.73
- 收稿：2022-10-28，
  
  修回：2023-04-06，
  
  纸质出版：2023-05-25
- 稿件说明：
移动端阅览
马宾,王一利,徐健等.基于双向生成对抗网络的图像感知哈希算法[J].电子学报,2023,51(05):1405-1412.

MA Bin,WANG Yi-li,XU Jian,et al.An Image Perceptual Hash Algorithm Based on Bidirectional Generative Adversarial Network[J].ACTA ELECTRONICA SINICA,2023,51(05):1405-1412.
马宾,王一利,徐健等.基于双向生成对抗网络的图像感知哈希算法[J].电子学报,2023,51(05):1405-1412. DOI： 10.12263/DZXB.20221224.

MA Bin,WANG Yi-li,XU Jian,et al.An Image Perceptual Hash Algorithm Based on Bidirectional Generative Adversarial Network[J].ACTA ELECTRONICA SINICA,2023,51(05):1405-1412. DOI： 10.12263/DZXB.20221224.

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.

Abstract

An unsupervised perceptual hash generation algorithm based on a bidirectional generative adversarial network (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 training 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 authentication and source detection capabilities than other state-of-the-art schemes.

关键词

Keywords

references

MA B , SHI Y Q . A reversible data hiding scheme based on code division multiplexing [J]. IEEE Transactions on Information Forensics and Security , 2016 , 11 ( 9 ): 1914 - 1927 .

MA B , CHANG L L , WANG C P , et al . Robust image watermarking using invariant accurate polar harmonic Fourier moments and chaotic mapping [J]. Signal Processing , 2020 , 172 : 107544 .

SRIVASTAVA M , SIDDIQUI J , et al . Local binary pattern based technique for content based image copy detection [C]// 2020 International Conference on Power Electronics & IoT Applications in Renewable Energy and its Control (PARC) . Piscataway : IEEE , 2020 : 374 - 377 .

QIN C , LIU E L , FENG G R , et al . Perceptual image hashing for content authentication based on convolutional neural network with multiple constraints [J]. IEEE Transactions on Circuits and Systems for Video Technology , 2020 , 31 ( 11 ): 4523 - 4537 .

SCHNEIDER M , CHANG S F . A robust content based digital signature for image authentication [C]// Proceedings of 3rd IEEE International Conference on Image Processing . Piscataway : IEEE , 2002 , 3 : 227 - 230 .

ZHAO Y , YUAN X R . Perceptual image hashing based on color structure and intensity gradient [J]. IEEE Access , 2020 , 8 : 26041 - 26053 .0-250.

TANG Z J , ZHANG X Q , et al . Robust image hashing with ring partition and invariant vector distance [J]. IEEE transactions on information forensics and security , 2015 , 11 ( 1 ): 200 - 214 .

SRIVASTAVA M , SIDDIQUI J , ALI M A . Robust image hashing based on statistical features for copy detection [C]// 2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON) . Piscataway : IEEE , 2017 : 490 - 495 .

ZHAO Y , WANG S Z , ZHANG X P , et al . Robust hashing for image authentication using Zernike moments and local features [J]. IEEE transactions on information forensics and security , 2013 , 8 ( 1 ): 55 - 63 .

CHEN Y C , YU W Y , FENG J C . Robust image hashing using invariants of Tchebichef moments [J]. Optik , 2014 , 125 ( 19 ): 5582 - 5587 .

YUAN X R , ZHAO Y . Perceptual image hashing based on three-dimensional global features and image energy [J]. IEEE Access , 2021 , 9 : 49325 - 49337 .

HOSNY K M , KHEDR Y M , KHEDR W I , et al . Robust image hashing using exact Gaussian-Hermite moments [J]. IET Image Processing , 2018 , 12 ( 12 ): 2178 - 2185 .

OUYANG J , LIU Y Z , SHU H Z . Robust hashing for image authentication using SIFT feature and quaternion Zernike moments [J]. Multimedia Tools and Applications , 2017 , 76 ( 2 ): 2609 - 2626 .

WANG X F , XUE J R , ZHENG Z Q , et al . Image forensic signature for content authenticity analysis [J]. Journal of Visual Communication and Image Representation , 2012 , 23 ( 5 ): 782 - 797 .

TANG Z J , HUANG L Y , ZHANG X Q , et al . Robust image hashing based on color vector angle and Canny operator [J]. AEU-International Journal of Electronics and Communications , 2016 , 70 ( 6 ): 833 - 841 .

VADLAMUDI L N , VADDELLA R P V , DEVARA V . Robust image hashing using SIFT feature points and DWT approximation coefficients [J]. ICT Express , 2018 , 4 ( 3 ): 154 - 159 .

LIN C Y , CHANG S F . A robust image authentication method distinguishing JPEG compression from malicious manipulation [J]. IEEE Transactions on Circuits and Systems for Video Technology , 2001 , 11 ( 2 ): 153 - 168 .

TANG Z J , Yang F , HUANG L Y , et al . Robust image hashing with dominant DCT coefficients [J]. Optik , 2014 , 125 ( 18 ): 5102 - 5107 .

HUANG Z Q , LIU S G . Perceptual image hashing with texture and invariant vector distance for copy detection [J]. IEEE Transactions on Multimedia , 2020 , 23 : 1516 - 1529 .

VENKATESAN R , KOON S M , JAKUBOWSKI M H , et al . Robust image hashing [C]//Proceedings 2000 International Conference on Image Processing (Cat. No. 00CH37101) . Piscataway : IEEE , 2002 , 3 : 664 - 666 .

TANG Z J , LING M , YAO H , et al . Robust image hashing via random Gabor filtering and DWT [J]. CMC-Computers Materials & Continua , 2018 , 55 ( 2 ): 331 - 344 .

SWAMINATHAN A , MAO Y N , WU M . Robust and secure image hashing [J]. IEEE Transactions on Information Forensics and security , 2006 , 1 ( 2 ): 215 - 230 .

QIN C , CHANG C C , TSOU P L . Robust image hashing using non-uniform sampling in discrete Fourier domain [J]. Digital Signal Processing , 2013 , 23 ( 2 ): 578 - 585 .

KOZAT S S , VENKATESAN R , MIHÇAK M K . Robust perceptual image hashing via matrix invariants [C]// 2004 International Conference on Image Processing , 2004 . ICIP' 04. Piscataway : IEEE , 2005, 5 : 3443 - 3446 .

TANG Z J , ZHANG X Q , ZHANG S C . Robust perceptual image hashing based on ring partition and NMF [J]. IEEE transactions on knowledge and data engineering , 2013 , 26 ( 3 ): 711 - 724 .

TANG Z J , RUAN L L , QIN C , et al . Robust image hashing with embedding vector variance of LLE [J]. Digital Signal Processing , 2015 , 43 : 17 - 27 .

TANG Z J , LAO H , ZHANG X Q , et al . Robust image hashing via DCT and LLE [J]. Computers & Security , 2016 , 62 : 133 - 148 .

ZHU X F , LI X L , ZHANG S C , et al . Graph PCA hashing for similarity search [J]. IEEE Transactions on Multimedia , 2017 , 19 ( 9 ): 2033 - 2044 .

LI Y N , WANG D D , TANG L L . Robust and secure image fingerprinting learned by neural network [J]. IEEE Transactions on Circuits and Systems for Video Technology , 2020 , 30 ( 2 ): 362 - 375 .

DONAHUE J , KRÄHENBÜHL P , DARRELL T . Adversarial feature learning [EB/OL]. [ 2023-04-11 ]. https://arxiv.org/abs/1605.09782 https://arxiv.org/abs/1605.09782 .

HUANG Z Q , TANG Z J , ZHANG X Q , et al . Perceptual image hashing with locality preserving projection for copy detection [J]. IEEE Transactions on Dependable and Secure Computing , 2023 , 20 ( 1 ): 463 - 477 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于注意力机制优化的生成对抗网络及其在海杂波模拟中的应用

天波超视距雷达地海杂波图像增强与检测器设计

基于最小熵准则与生成对抗网络的SAR三维转动舰船目标重聚焦方法

带有故障性质预测的自动测试向量求解模型