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Binary multi-view perceptual hashing for image authentication

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Abstract

This paper presents a novel Binary Multi-View Perceptual Hashing (BMVPH) scheme for image authentication, which provides compact and efficient representations and can easily scale to large data. We apply virtual prior attacks (e.g. additive noise, blurring, compression, logo-insert etc.) on original images to generate simulated distorted copies. The original images and the corresponding distorted copies provide the so-called training set. For perceptual hashing learning, we formulate BMVPH by two key components: collaborative binary representation learning (CBRL) and perpetual content authentication learning (PCAL), into a unified learning framework. Our BMVPH scheme collaboratively encodes the multi-view features into a compact common binary code space while considering the perceptual content similarity at the same time. The experimental results show that when compared with the state-of-the-art methods, the proposed algorithm can achieve higher discrimination and better perceptual robustness. In particular, the Area Under ROC Curve (AUC) increases on average of 3.8% as compared with other state-of-the-art methods.

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Acknowledgments

This work was supported by National Natural Science Foundation of China (Grant No. 61602344), Science&Technology Development Fund of Tianjin Education Commission for Higher Education, China (Grant No. 2017KJ091) and Natural Science Foundation of Tianjin (Grant No. 17JCQNJC00600).

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Authors and Affiliations

  1. School of Computer Science and Technology, Tianjin Polytechnic University, Tianjin, 300387, China

    Ling Du & Zhen Chen

  2. Department of Computer Science, University of Surrey, Guildford, Surrey, GU2 7XH, UK

    Anthony T. S. Ho

  3. Tianjin University of Science and Technology, Tianjin, 300457, China

    Anthony T. S. Ho

  4. Wuhan University of Technology, Wuhan, 430070, China

    Anthony T. S. Ho

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  1. Ling Du
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Correspondence to Ling Du.

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Du, L., Chen, Z. & Ho, A.T.S. Binary multi-view perceptual hashing for image authentication. Multimed Tools Appl 80, 22927–22949 (2021). https://doi.org/10.1007/s11042-020-08736-6

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