Perceptual Image Hashing Based on Multitask Neural Network

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.

A Novel Approach for Robust Perceptual Image Hashing

Perceptual image hashing system generates a short signature called perceptual hash attached to an image before transmission and acts as side information for analyzing the trustworthiness of the received image. In this paper, we propose a novel approach to improve robustness for perceptual image hashing scheme for generating a perceptual hash that should be resistant to content-preserving manipulations, such as JPEG compression and Additive white Gaussian noise (AWGN) also should differentiate the maliciously tampered image and its original version. Our algorithm first constructs a robust image, derived from the original input by analyzing the stability of the extracted features and improving their robustness. From the robust image, which does perceptually resemble the original input, we further extract the final robust features. Next, robust features are suitably quantized allowing the generation of the final perceptual hash using the cryptographic hash function SHA1. The main idea of this paper is to transform the original image into a more robust one that allows the extraction of robust features. Generation of the robust image turns out be quite important since it introduces further robustness to the perceptual image hashing system. The paper can be seen as an attempt to propose a general methodology for more robust perceptual image hashing. The experimental results presented in this paper reveal that the proposed scheme offers good robustness against JPEG compression and Additive white Gaussian noise.