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Robust image hashing using progressive feature selection for tampering detection

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Abstract

The main problem addressed in this paper is the robust tamper detection of the image received in a transmission under various content-preserving attacks. To this aim the progressive feature point selection method is proposed to extract the feature points of high robustness; with which, the local feature and color feature are then generated for each feature point. Afterwards, the robust image hashing construction method is proposed by using the location-context information of the features. The constructed hash is attached to the image before transmission and it can be used for analyzing at destination to filter out the geometric transformations occurred in the received image. After image restoration, the similarity of the global hashes between the source image and restored image is calculated to determine whether the received image has the same contents as the trusted one or has been maliciously tampered. When the received image being judged as a tampered image, the hashes calculated with the proposed Horizontal Location-Context Hashing (HLCH) and Vertical Location-Context Hashing (VLCH) methods will be used to locate the tampered regions. Experimental results on different images with tampering of arbitrary size and location demonstrate that our image authentication and tampering localization scheme are superior to the state-of-the-art methods under various attacks.

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Acknowledgements

This research was supported in part by the Research Committee of the University of Macau (MYRG2015-00011-FST, MYRG2015-00012-FST) and the Science and Technology Development Fund of Macau SAR (093-2014-A2).

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Authors and Affiliations

  1. Department of Computer and Information Science, University of Macau, Macau, SAR, China

    Chi-Man Pun & Xiao-Chen Yuan

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  1. Chi-Man Pun
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  2. Cai-Ping Yan
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  3. Xiao-Chen Yuan
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Correspondence to Chi-Man Pun.

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Pun, CM., Yan, CP. & Yuan, XC. Robust image hashing using progressive feature selection for tampering detection. Multimed Tools Appl 77, 11609–11633 (2018). https://doi.org/10.1007/s11042-017-4809-4

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  • DOI: https://doi.org/10.1007/s11042-017-4809-4

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