TY - JOUR
T1 - Digital forensics of printed source identification for Chinese characters
AU - Tsai, Min Jen
AU - Yin, Jin Shen
AU - Yuadi, Imam
AU - Liu, Jung
N1 - Funding Information:
Acknowledgements This work was supported by the National Science Council in Taiwan, Republic of China, under Grant NSC99-2410-H-009-053-MY2 and NSC101-2410-H-009-006-MY2.
Publisher Copyright:
© 2013, Springer Science+Business Media New York.
PY - 2014/10/29
Y1 - 2014/10/29
N2 - Recently, digital forensics, which involves the collection and analysis of the origin digital device, has become an important issue. Digital content can play a crucial role in identifying the source device, such as serve as evidence in court. To achieve this goal, we use different texture feature extraction methods such as graylevel co-occurrence matrix (GLCM) and discrete wavelet transform (DWT), to analyze the Chinese printed source in order to find the impact of different output devices. Furthermore, we also explore the optimum feature subset by using feature selection techniques and use support vector machine (SVM) to identify the source model of the documents. The average experimental results attain a 98.64 % identification rate which is significantly superior to the existing known method of GLCM by 1.27 %. The superior testing performance demonstrates that the proposed identification method is very useful for source laser printer identification.
AB - Recently, digital forensics, which involves the collection and analysis of the origin digital device, has become an important issue. Digital content can play a crucial role in identifying the source device, such as serve as evidence in court. To achieve this goal, we use different texture feature extraction methods such as graylevel co-occurrence matrix (GLCM) and discrete wavelet transform (DWT), to analyze the Chinese printed source in order to find the impact of different output devices. Furthermore, we also explore the optimum feature subset by using feature selection techniques and use support vector machine (SVM) to identify the source model of the documents. The average experimental results attain a 98.64 % identification rate which is significantly superior to the existing known method of GLCM by 1.27 %. The superior testing performance demonstrates that the proposed identification method is very useful for source laser printer identification.
KW - Digital image forensics
KW - Discrete Wavelet Transform (DWT)
KW - Feature Selection
KW - Graylevel co-occurrence Matrix (GLCM)
UR - http://www.scopus.com/inward/record.url?scp=84911995740&partnerID=8YFLogxK
U2 - 10.1007/s11042-013-1642-2
DO - 10.1007/s11042-013-1642-2
M3 - Article
AN - SCOPUS:84911995740
SN - 1380-7501
VL - 73
SP - 2129
EP - 2155
JO - Multimedia Tools and Applications
JF - Multimedia Tools and Applications
IS - 3
ER -