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

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.