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Image tamper detection based on noise estimation and lacunarity texture

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Abstract

Aiming at the problem of image tampering, a novel detection method is proposed based on the image noise and lacunarity. As there exist differences in image sensor pattern noise and image lacunarity between real image and tampered image, standard deviation of noise, relative frequency lacunarity (RFL), relative frequency mean (RFM) and relative frequency variance (RFV) are extracted from the suspected image to construct feature space. By using LIBSVM classifier, the image is detected if it is tampered or not. Experimental results and analysis show that it can effectively be used for the detection of real image and tampered image, natural image and computer generated graphics. Furthermore, it can be implemented for the detection of artificial blurring in the image with high precision.

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Authors and Affiliations

  1. School of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China

    Qiuwei Yang, Fei Peng & Jiao-Ting Li

  2. School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing, 210044, China

    Fei Peng

  3. College of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410014, China

    Min Long

Authors
  1. Qiuwei Yang
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  2. Fei Peng
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  3. Jiao-Ting Li
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  4. Min Long
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Corresponding author

Correspondence to Fei Peng.

Additional information

This work was partly supported by the National Science Foundation of China (Grant No. 61572182, 61370225, 61300036), the Projects in the National Science & Technology Pillar Program (Grant No.2013BAH38F01), and Hunan Provincial Natural Science Foundation of China (Grant No.15JJ2007), and the Scientific Research Plan of Hunan Provincial Science and Technology Department of China (2014FJ4161), the Foundation for University Key Teacher by the Ministry of Education.

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Yang, Q., Peng, F., Li, JT. et al. Image tamper detection based on noise estimation and lacunarity texture. Multimed Tools Appl 75, 10201–10211 (2016). https://doi.org/10.1007/s11042-015-3079-2

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  • DOI: https://doi.org/10.1007/s11042-015-3079-2

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