文献类型：期刊文献

英文题名：Perceptual image hashing based on a deep convolution neural network for content authentication

作者：Jiang, Cuiling[1];Pang, Yilin[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai, Peoples R China

年份：2018

卷号：27

期号：4

外文期刊名：JOURNAL OF ELECTRONIC IMAGING

收录：;EI(收录号：20183705796965);WOS:【SCI-EXPANDED(收录号:WOS:000445139400007)】；

基金：The authors are sincerely grateful for the anonymous reviewers' insightful comments and valuable suggestions, which substantially improved the quality of this study. Many thanks for Zhou-mao Kang and Chenchen's experiment testing. This work was partially supported by the National Natural Science Foundation of China (No. 61371150).

语种：英文

外文关键词：image hashing; deep convolutional neural network; feature matrix; content authentication; discrimination

摘要：Image hash functions have wide-ranging application in many fields. This study presents a perceptual image-hashing scheme for a deep convolutional neural network (DCNN) for the purpose of content authentication. First, an AlexNet model of DCNN is constructed and trained to assess the performance of a given network. Then, the trained network is used to extract an image feature matrix. Finally, an image-hashing series is generated for content authentication. Experimental results show that, compared with other methods, the proposed method has a higher discrimination capability and an acceptable robustness against content-preserving operations, such as random attacks, rotation, JPEG compression, and additive Gaussian noise. A receiver operating characteristics curve is employed and demonstrates that the proposed image hashing obtains a desirable compromise between discrimination and robustness. (C) 2018 SPIE and IS&T