TY - GEN
T1 - Digital forensics of printed source identification for Chinese characters
AU - Tsai, Min Jen
AU - Liu, Jung
AU - Yin, Jin Sheng
AU - Yuadi, Imam
N1 - Funding Information:
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.
PY - 2014
Y1 - 2014
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 gray-level 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 using 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 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 gray-level 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 using 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 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=84904733148&partnerID=8YFLogxK
U2 - 10.1007/978-3-662-43886-2_25
DO - 10.1007/978-3-662-43886-2_25
M3 - Conference contribution
AN - SCOPUS:84904733148
SN - 9783662438855
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 337
EP - 361
BT - Digital-Forensics and Watermarking - 12th International Workshop, IWDW 2013, Revised Selected Papers
PB - Springer Verlag
T2 - 12th International Workshop on Digital-Forensics and Watermarking, IWDW 2013
Y2 - 1 October 2013 through 4 October 2013
ER -