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Source Camera Identification Issues
Author(s) -
Yongjian Hu,
ChangTsun Li,
Changhui Zhou,
Xufeng Lin
Publication year - 2011
Publication title -
international journal of digital crime and forensics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.192
H-Index - 15
eISSN - 1941-6229
pISSN - 1941-6210
DOI - 10.4018/jdcf.2011100101
Subject(s) - computer science , artificial intelligence , identification (biology) , classifier (uml) , computer vision , sample (material) , pattern recognition (psychology) , camera resectioning , camera auto calibration , chemistry , botany , biology , chromatography
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 sample 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. Copyright © 2011, IGI Global.

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