Perceptual image hashing based on a deep convolution neural network for content authentication

Cuiling Jiang; Yilin Pang

doi:10.1117/1.JEI.27.4.043055

29 August 2018 Perceptual image hashing based on a deep convolution neural network for content authentication

Cuiling Jiang, Yilin Pang

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 4, 043055 (August 2018). https://doi.org/10.1117/1.JEI.27.4.043055

Abstract

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.

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Cuiling Jiang and Yilin Pang "Perceptual image hashing based on a deep convolution neural network for content authentication," Journal of Electronic Imaging 27(4), 043055 (29 August 2018). https://doi.org/10.1117/1.JEI.27.4.043055

Received: 17 March 2018; Accepted: 30 July 2018; Published: 29 August 2018

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