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Advances in Multimedia
Volume 2018, Article ID 4235268, 11 pages
Research Article

Image Hashing for Tamper Detection with Multiview Embedding and Perceptual Saliency

School of Computer Science and Software Engineering, Tianjin Key Laboratory of Optoelectronic Detection Technology and System, Tianjin Polytechnic University, Tianjin 300387, China

Correspondence should be addressed to Ling Du; nc.ude.upjt@gnilud

Received 29 March 2018; Accepted 3 October 2018; Published 19 November 2018

Academic Editor: Kjell Brunnström

Copyright © 2018 Ling Du et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Perceptual hashing technique for tamper detection has been intensively investigated owing to the speed and memory efficiency. Recent researches have shown that leveraging supervised information could lead to learn a high-quality hashing code. However, most existing methods generate hashing code by treating each region equally while ignoring the different perceptual saliency relating to the semantic information. We argue that the integrity for salient objects is more critical and important to be verified, since the semantic content is highly connected to them. In this paper, we propose a Multi-View Semi-supervised Hashing algorithm with Perceptual Saliency (MV-SHPS), which explores supervised information and multiple features into hashing learning simultaneously. Our method calculates the image hashing distance by taking into account the perceptual saliency rather than directly considering the distance value between total images. Extensive experiments on benchmark datasets have validated the effectiveness of our proposed method.