Video Tamper Detection Based on Convolutional Neural Network and Perceptual Hashing Learning

Abstract

Perceptual hashing has been widely used in the field of multimedia security. The difficulty of the traditional perceptual hashing algorithm is to find suitable perceptual features. In this paper, we propose a perceptual hashing learning method for tamper detection based on convolutional neural network, where a hashing layer in the convolutional neural network is introduced to learn the features and hash functions. Specifically, the video is decomposed to obtain temporal representative frame (TRF) sequences containing temporal and spatial domain information. Convolutional neural network is then used to learn visual features of each TRF. We further put each feature into the hashing layer to learn independent hash functions and fuse these features to generate the video hash. Finally, the hash functions and the corresponding video hash are obtained by minimizing the classification loss and quantization error loss. Experimental results and comparisons with state-of-the-art methods show that the algorithm has better classification performance and can effectively perform tamper detection.