华南理工大学学报(自然科学版)

• 计算机科学与技术 • 上一篇    下一篇

基于深度卷积神经网络的图像哈希认证方法

蒋翠玲 庞毅林 林家骏 康周茂    

  1. 华东理工大学 信息科学与工程学院,上海 200237
  • 收稿日期:2018-01-30 出版日期:2018-05-25 发布日期:2018-04-03
  • 通信作者: 蒋翠玲( 1976-) ,女,博士,讲师. 主要从事信息隐藏研究 E-mail:cuilingjiang@ecust.edu.cn
  • 作者简介:蒋翠玲( 1976-) ,女,博士,讲师. 主要从事信息隐藏研究
  • 基金资助:
     国家自然科学基金资助项目( 61371150) 

Image Hashing Authentication Method Based on
Deep Convolution Neural Network
 

JIANG Cuiling PANG Yilin LIN Jiajun KANG Zhoumao 
  

  1. School of Information Science and Engineering,East China University of Science and Technology,Shanghai 200237,China
  • Received:2018-01-30 Online:2018-05-25 Published:2018-04-03
  • Contact: 蒋翠玲( 1976-) ,女,博士,讲师. 主要从事信息隐藏研究 E-mail:cuilingjiang@ecust.edu.cn
  • About author:蒋翠玲( 1976-) ,女,博士,讲师. 主要从事信息隐藏研究
  • Supported by:
     Supported by the National Natural Science Foundation of China( 61371150) 

摘要: 提出了一种基于深度卷积神经网络的图像哈希认证方法. 首先构建深度卷积神 经网络 AlexNet 模型,训练该网络得到预定的网络性能; 然后由训练好的卷积神经网络提 取图像的特征,最后生成图像哈希序列用于图像内容的篡改认证. 实验结果表明,相比同 类方法,文中提出的图像哈希认证方法具有较高的区分性,同时对随机攻击、 JPEG 压缩、 加性高斯噪声等具有可接受的鲁棒性. ROC 曲线表明,文中提出的方法实现了区分性与 鲁棒性的均衡. 

关键词: 信息安全, 卷积神经网络, 图像哈希, 区分性, 鲁棒性 

Abstract:  This paper presents a scheme of deep convolution network for image hashing authentication. First,the AlexNet model of deep convolution network is constructed and the given network performance is achieved through training. Then, the trained network is used to extract image features and generate image-hashing series for content authentication. The experimental results show that in comparison with other methods,the proposed method has a higher discrimination and an acceptable robustness against content-preserving operations such as random attack, rotation,JPEG compression,and additive Gaussian noise. Receiver operating characteristics ( ROC) curve comparison demonstrates that the proposed method is able to attain a desirable compromise between the robustness and discrimination. 

Key words: information security, convolutional neural network, image hashing, discrimination, robustness

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