Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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How to identify fake images? : Multiscale methods vs. Sherlock Holmes

  • Park, Minsu (Department of Information and Statistics, Chungnam National University) ;
  • Park, Minjeong (Statistical Research Institute) ;
  • Kim, Donghoh (Department of Mathematics and Statistics, Sejong University) ;
  • Lee, Hajeong (Department of Internal Medicine, Seoul National University Hospital) ;
  • Oh, Hee-Seok (Department of Statistics, Seoul National University)
  • Received : 2021.03.21
  • Accepted : 2021.09.23
  • Published : 2021.11.30
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Abstract

In this paper, we propose wavelet-based procedures to identify the difference between images, including portraits and handwriting. The proposed methods are based on a novel combination of multiscale methods with a regularization technique. The multiscale method extracts the local characteristics of an image, and the distinct features are obtained through the regularized regression of the local characteristics. The regularized regression approach copes with the high-dimensional problem to build the relation between the local characteristics. Lytle and Yang (2006) introduced the detection method of forged handwriting via wavelets and summary statistics. We expand the scope of their method to the general image and significantly improve the results. We demonstrate the promising empirical evidence of the proposed method through various experiments.

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Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MIST) (No. 2021R1C1C1009976, 2018R1D1A1B07042933, and 2021R1F1A1047506).

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