TY - GEN
T1 - Adaptive feature selection for digital camera source identification
AU - Tsai, Min-Jen
AU - Wang, Cheng Sheng
PY - 2008
Y1 - 2008
N2 - Digital forensics which identifies the characteristics and the originality of the digital devices has lately become one of the very important applications. If it wants to serve as the evidence to the court like its traditional counterparts, the digital forensics issues like verifying the authenticity of digital image, detecting the forged regions or identifying digital camera source of an image will need to be addressed. This study has focused on analyzing the relationship between digital cameras and the photographs by using the support vector machine (SVM). In this paper, several feature selection algorithms will be implemented with SVM-based classifier to increase the predicting accuracy rate of identifying digital camera source of an image. The experiment results demonstrate that our adaptive feature selection scheme can achieve higher identification rate for unbiased camera sources than the results without using feature selection approaches.
AB - Digital forensics which identifies the characteristics and the originality of the digital devices has lately become one of the very important applications. If it wants to serve as the evidence to the court like its traditional counterparts, the digital forensics issues like verifying the authenticity of digital image, detecting the forged regions or identifying digital camera source of an image will need to be addressed. This study has focused on analyzing the relationship between digital cameras and the photographs by using the support vector machine (SVM). In this paper, several feature selection algorithms will be implemented with SVM-based classifier to increase the predicting accuracy rate of identifying digital camera source of an image. The experiment results demonstrate that our adaptive feature selection scheme can achieve higher identification rate for unbiased camera sources than the results without using feature selection approaches.
KW - Correlation
KW - Feature selection
KW - Image quality metrics
KW - Supporting vector machine
UR - http://www.scopus.com/inward/record.url?scp=51749124673&partnerID=8YFLogxK
U2 - 10.1109/ISCAS.2008.4541442
DO - 10.1109/ISCAS.2008.4541442
M3 - Conference contribution
AN - SCOPUS:51749124673
SN - 9781424416844
T3 - Proceedings - IEEE International Symposium on Circuits and Systems
SP - 412
EP - 415
BT - 2008 IEEE International Symposium on Circuits and Systems, ISCAS 2008
T2 - 2008 IEEE International Symposium on Circuits and Systems, ISCAS 2008
Y2 - 18 May 2008 through 21 May 2008
ER -