Issues on Selecting Image Features for Robust Source Camera Identification

Yongjian Hu; Chang-Tsun Li; Changhui Zhou; Xufeng Lin

Issues on Selecting Image Features for Robust Source Camera Identification

Yongjian Hu, Chang-Tsun Li, Changhui Zhou, Xufeng Lin

Research output: Book chapter/Published conference paper › Conference paper › peer-review

Abstract

Image feature selection is an important issue for source camera identification. Well-selected features should make camera classifiers accurate, efficient as well as robust. Current source camera identification schemes select image features mainly based on classification accuracy and computational efficiency. In this work, we demonstrate that robustness should also be considered for classifiers which aim at real-world tasks. Besides, we reveal what impact the reduced feature subset will have on the robustness of camera classifiers. The dimensionality reduction is often necessary for computational efficiency.

Original language	English
Title of host publication	Proceedings of the Sixth International Workshop on Digital Forensics and Incident Analysis
Subtitle of host publication	WDFIA 2011
Place of Publication	UK
Publisher	University of Plymouth
Pages	1-10
Number of pages	10
Publication status	Published - 2011
Event	6th Annual Workshop on Digital Forensics & Incident Analysis: WDFIA 2011 - Kingston University, London, United Kingdom Duration: 07 Jul 2011 → 08 Jul 2011 https://web.archive.org/web/20110807232633/http://www.wdfia.org/

Conference

Conference	6th Annual Workshop on Digital Forensics & Incident Analysis
Country	United Kingdom
City	London
Period	07/07/11 → 08/07/11
Internet address	https://web.archive.org/web/20110807232633/http://www.wdfia.org/

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Cite this

@inproceedings{6a8fc34b1c91409f8b8f09429403736c,

title = "Issues on Selecting Image Features for Robust Source Camera Identification",

abstract = "Image feature selection is an important issue for source camera identification. Well-selected features should make camera classifiers accurate, efficient as well as robust. Current source camera identification schemes select image features mainly based on classification accuracy and computational efficiency. In this work, we demonstrate that robustness should also be considered for classifiers which aim at real-world tasks. Besides, we reveal what impact the reduced feature subset will have on the robustness of camera classifiers. The dimensionality reduction is often necessary for computational efficiency.",

author = "Yongjian Hu and Chang-Tsun Li and Changhui Zhou and Xufeng Lin",

year = "2011",

language = "English",

pages = "1--10",

booktitle = "Proceedings of the Sixth International Workshop on Digital Forensics and Incident Analysis",

publisher = "University of Plymouth",

note = "6th Annual Workshop on Digital Forensics & Incident Analysis : WDFIA 2011 ; Conference date: 07-07-2011 Through 08-07-2011",

url = "https://web.archive.org/web/20110807232633/http://www.wdfia.org/",

}

Issues on Selecting Image Features for Robust Source Camera Identification. / Hu, Yongjian; Li, Chang-Tsun; Zhou, Changhui; Lin, Xufeng.

Proceedings of the Sixth International Workshop on Digital Forensics and Incident Analysis: WDFIA 2011. UK : University of Plymouth, 2011. p. 1-10.

Research output: Book chapter/Published conference paper › Conference paper › peer-review

TY - GEN

T1 - Issues on Selecting Image Features for Robust Source Camera Identification

AU - Hu, Yongjian

AU - Li, Chang-Tsun

AU - Zhou, Changhui

AU - Lin, Xufeng

PY - 2011

Y1 - 2011

N2 - Image feature selection is an important issue for source camera identification. Well-selected features should make camera classifiers accurate, efficient as well as robust. Current source camera identification schemes select image features mainly based on classification accuracy and computational efficiency. In this work, we demonstrate that robustness should also be considered for classifiers which aim at real-world tasks. Besides, we reveal what impact the reduced feature subset will have on the robustness of camera classifiers. The dimensionality reduction is often necessary for computational efficiency.

AB - Image feature selection is an important issue for source camera identification. Well-selected features should make camera classifiers accurate, efficient as well as robust. Current source camera identification schemes select image features mainly based on classification accuracy and computational efficiency. In this work, we demonstrate that robustness should also be considered for classifiers which aim at real-world tasks. Besides, we reveal what impact the reduced feature subset will have on the robustness of camera classifiers. The dimensionality reduction is often necessary for computational efficiency.

M3 - Conference paper

SP - 1

EP - 10

BT - Proceedings of the Sixth International Workshop on Digital Forensics and Incident Analysis

PB - University of Plymouth

CY - UK

T2 - 6th Annual Workshop on Digital Forensics & Incident Analysis

Y2 - 7 July 2011 through 8 July 2011

ER -