Perceptual Image Hashing Based on Multitask Neural Network

Abstract

With the advent of the era of multimedia and in-depth development, the whole human society has been produced and spread a huge amount of image data, but at the same time, in view of the digital image and tamper with the attack of piracy phenomenon also more and more serious, malicious attacks will produce serious social, military, and political influence, therefore, to protect the authenticity of the original image content, which is also more and more important. In order to further improve the performance of image hashing and enhance the protection of image data, we proposed an end-to-end dual-branch multitask neural network based on VGG-19 to produce a perceptual hash sequence and used prepart of network of pretrained VGG-19 model to extract image features, and then, the image features are transformed into a hash sequence through a convolutional and fully connected network. At the same time, in order to enhance the function of the network and improve the adaptability of the proposed network to using scenarios, the rest part of the network layer of the VGG-19 model was used as another branch for image classification, so as to realize the multitask characteristics of the network. Through the experiment of the testing set, the network can not only resist many kinds of attack operations (content retention operations), but also realize accurate classification about the image, and has a satisfactory tampering detection ability.