ORCID Profile
0000-0002-3400-8700
Current Organisations
Charles Sturt University
,
Deakin University
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Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2016
Publisher: IEEE
Date: 09-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: Springer Singapore
Date: 2019
Publisher: IGI Global
Date: 10-2011
Abstract: Statistical image features play an important role in forensic identification. Current source camera identification schemes select image features mainly based on classification accuracy and computational efficiency. For forensic investigation purposes however, these selection criteria are not enough. Consider most real-world photos may have undergone common image processing due to various reasons, source camera classifiers must have the capability to deal with those processed photos. In this work, the authors first build a s le camera classifier using a combination of popular image features, and then reveal its deficiency. Based on the experiments, suggestions for the design of robust camera classifiers are given.
Publisher: Institution of Engineering and Technology
Date: 12-04-2018
Publisher: IEEE
Date: 07-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2016
Publisher: SPIE
Date: 19-02-2014
DOI: 10.1117/12.2038644
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: IEEE
Date: 11-2019
Publisher: Elsevier BV
Date: 03-2023
Publisher: IEEE
Date: 07-2016
Publisher: Springer Science and Business Media LLC
Date: 16-10-2017
No related grants have been discovered for Xufeng Lin.