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Noisy Smoothing Image Source Identification

  • Yuying Liu
  • Yonggang Huang
  • Jun Zhang
  • Xu Liu
  • Hualei Shen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10581)

Abstract

Feature based image source identification plays an important role in the toolbox for forensics investigations on images. Conventional feature based identification schemes suffer from the problem of noise, that is, the training dataset contains noisy samples. To address this problem, we propose a new Noisy Smoothing Image Source Identification (NS-ISI) method. NS-ISI address the noise problem in two steps. In step 1, we employ a classifier ensemble approach for noise level evaluation for each training sample. The noise level indicates the probability of being noisy. In step 2, a noise sensitive sampling method is employed to sample training samples from original training set according to the noise level, producing a new training dataset. The experiments carried out on the Dresden image collection confirms the effectiveness of the proposed NS-ISI. When the noisy samples present, the identification accuracy of NS-ISI is significantly better than traditional methods.

Notes

Acknowledgment

This work was supported by the National Natural Science Foundation of China (No. 61300077 & No. 61502319).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yuying Liu
    • 1
  • Yonggang Huang
    • 1
  • Jun Zhang
    • 2
  • Xu Liu
    • 1
  • Hualei Shen
    • 3
  1. 1.School of Computer Science and Technology, Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing ApplicationsBeijing Institute of TechnologyBeijingChina
  2. 2.School of Information TechnologyDeakin UniversityMelbourneAustralia
  3. 3.College of Computer and Information EngineeringHenan Normal UniversityXinxiangChina

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