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A Novel Robust Image Forensics Algorithm Based on L1-Norm Estimation

  • Xin He
  • Qingxiao Guan
  • Yanfei Tong
  • Xianfeng Zhao
  • Haibo Yu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10082)

Abstract

To improve the robustness of the typical image forensics with the noise variance, we propose a novel image forensics approach that based on L1-norm estimation. First, we estimate the kurtosis and the noise variance of the high-pass image. Then, we build a minimum error objective function based on L1-norm estimation to compute the kurtosis and the noise variance of overlapping blocks of the image by an iterative solution. Finally, the spliced regions are exposed through K-means cluster analysis. Since the noise variance of adjacent blocks are similar, our approach can accelerate the iterative process by setting the noise variance of the previous block as the initial value of the current block. According to analytics and experiments, our approach can effectively solve the inaccurate locating problem caused by outliers. It also performs better than reference algorithm in locating spliced regions, especially for those with realistic appearances, and improves the robustness effectively.

Keywords

Image splicing L1-norm estimation Noise variance Image forensics 

Notes

Acknowledgments

This work was supported by the NSFC under U1536105 and 61303259, National Key Technology R&D Program under 2014BAH41B01, Strategic Priority Research Program of CAS under XDA06030600, and Key Project of Institute of Information Engineering, CAS, under Y5Z0131201.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Xin He
    • 1
    • 2
  • Qingxiao Guan
    • 1
    • 2
  • Yanfei Tong
    • 1
    • 2
  • Xianfeng Zhao
    • 1
    • 2
  • Haibo Yu
    • 1
    • 2
  1. 1.State Key Laboratory of Information SecurityInstitute of Information Engineering, Chinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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