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Identifying natural images and computer generated graphics based on binary similarity measures of PRNU

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Abstract

Aiming at the identification of natural images and computer generated graphics, an image source pipeline forensics method based on binary similarity measures of PRNU (photo response non-uniformity) is proposed. As PRNU is a unique attribute of natural images, binary similarity measures of PRNU are used to represent the differences between natural images and computer generated graphics. Binary Kullback-Leibler distance, binary minimum histogram distance, binary absolute histogram distance and binary mutual entropy are calculated from PRNU in RGB three channels. With a total of 36 dimensions of features, LIBSVM is used for classification. Experimental results and analysis indicate that it can achieve an average identification accuracy of 99.83%, and the capability of identifying natural images and computer generated graphics is balanced. Meanwhile, it is robust against JPEG compression, rotation and additive noise.

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Acknowledgements

This work was supported in part by project supported by National Natural Science Foundation of China (Grant No. 61572182, 61370225), project supported by Hunan Provincial Natural Science Foundation of China (Grant No.15JJ2007), supported by the Scientific Research Plan of Hunan Provincial Science and Technology Department of China (2014FJ4161).

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Authors and Affiliations

  1. College of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, Hunan, 410014, People’s Republic of China

    Min Long

  2. Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha, Hunan, 410114, China

    Min Long

  3. College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, 410082, People’s Republic of China

    Fei Peng & Yin Zhu

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  1. Min Long
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  2. Fei Peng
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  3. Yin Zhu
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Correspondence to Fei Peng.

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Long, M., Peng, F. & Zhu, Y. Identifying natural images and computer generated graphics based on binary similarity measures of PRNU. Multimed Tools Appl 78, 489–506 (2019). https://doi.org/10.1007/s11042-017-5101-3

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  • DOI: https://doi.org/10.1007/s11042-017-5101-3

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