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Multi-granularity geometrically robust video hashing for tampering detection

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Abstract

The wide-spread video editing tools make it much easier to tamper a video, which raises a huge need for authentication techniques that can prove the originality of video content and locate the tampered regions on the video sequences. In this paper, a multi-granularity geometrically robust video hashing method is proposed for tampering detection and localization. In order to balance the robustness and sensitiveness, we describe a video from three levels of granularity: frame sequence level, block level and pixel level, and then hashes are generated at these three levels. Polar Complex Exponential Transform (PCET) moments are calculated on the low-pass sub-band of 3D Discrete Wavelet Transform (3D–DWT) on frame sequence to extract geometric invariant spatio-temporal hash, which is used for video authentication. Local PCET moments are calculated on annular and angular blocks, which are used for geometric correction and coarse tampering localization. Position information of salient objects is obtained from saliency map for fine tampering localization. Experimental results show that the proposed method is robust against temporal de-synchronization and geometrical transformation, and has high tampering localization accuracy even when the video is rotated. Compared with state-of-the-art methods, it is more robust against content-preserving operations and more sensitive to malicious manipulations.

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Acknowledgements

This paper is supported by National Natural Science Foundation of Guangdong (No.2016A030313472); National Natural Science Foundation of China (Grant No. 61472145); Guangdong Province Special funds for University-Industry Cooperation (No. 2013B090500015); Science and Technology Planning Project of Guangdong Province, China (No. 2016B090918042).

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Authors and Affiliations

  1. College of Computer Science and Engineering, South China University of Technology, Guangzhou, 510641, China

    Haichao Chen, Yan Wo & Guoqiang Han

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  1. Haichao Chen
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  2. Yan Wo
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  3. Guoqiang Han
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Correspondence to Yan Wo.

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Chen, H., Wo, Y. & Han, G. Multi-granularity geometrically robust video hashing for tampering detection. Multimed Tools Appl 77, 5303–5321 (2018). https://doi.org/10.1007/s11042-017-4434-2

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  • DOI: https://doi.org/10.1007/s11042-017-4434-2

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