An Image Perceptual Hash Algorithm Based on Bidirectional Generative Adversarial Network

MA Bin; WANG Yi-li; XU Jian; WANG Chun-peng; LI Jian; ZHOU Lin-na

doi:10.12263/DZXB.20221224

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An Image Perceptual Hash Algorithm Based on Bidirectional Generative Adversarial Network

PAPERS | 更新时间：2025-12-08

- An Image Perceptual Hash Algorithm Based on Bidirectional Generative Adversarial Network
- ACTA ELECTRONICA SINICA Vol. 51, Issue 5, Pages: 1405-1412(2023)
- 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：
- 基金信息：
  
  National Natural Science Foundation of China(62272255;61872203);National Key R & D Program of China(2021YFC3340600);Shandong Natural Science Foundation(ZR2019BF017;ZR2020MF054);Shandong Provincial Natural Science Foundation Innovation and Development Joint Fund(ZR202208310038)
- DOI：10.12263/DZXB.20221224
  CLC： TN911.73
- Received：28 October 2022，
  
  Revised：2023-04-06，
  
  Published：25 May 2023
- 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.

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references

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