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Noisy smoothing image source identification

conference contribution
posted on 2017-01-01, 00:00 authored by Y Liu, Y Huang, Jun Zhang, X Liu, H Shen
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.

History

Event

Cyberspace Safety and Security. Symposium (9th : 2017 : Xi'an, China)

Series

Cyberspace Safety and Security Symposium

Pagination

135 - 147

Publisher

Springer

Location

Xi'an, China

Place of publication

Cham, Switzerland

Start date

2017-10-23

End date

2017-10-25

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783319694702

Language

eng

Publication classification

E Conference publication; E1 Full written paper - refereed

Copyright notice

2017, Springer International Publishing AG

Editor/Contributor(s)

Sheng Wen, Wei Wu, Aniello Castiglione

Title of proceedings

CSS 2017 : Proceedings of the 9th International Symposium on Cyberspace Safety and Security 2017