Digital forensics for printed source identification

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 source of printers. 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 1.2%. This higher testing performance demonstrates that the proposed identification method is very useful for source laser printer identification.

[1]  Jan P. Allebach,et al.  Printer identification based on graylevel co-occurrence features for security and forensic applications , 2005, IS&T/SPIE Electronic Imaging.

[2]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[3]  Heung-Kyu Lee,et al.  Color laser printer identification by analyzing statistical features on discrete wavelet transform , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[4]  Min-Jen Tsai,et al.  Source color laser printer identification using discrete wavelet transform and feature selection algorithms , 2011, 2011 IEEE International Symposium of Circuits and Systems (ISCAS).

[5]  Ingemar J. Cox,et al.  Digital Watermarking and Steganography , 2014 .

[6]  Deepa Kundur,et al.  Special Issue on Enabling Security Technologies for Digital Rights Management , 2004, Proc. IEEE.

[7]  Josef Kittler,et al.  Floating search methods in feature selection , 1994, Pattern Recognit. Lett..

[8]  Mohan S. Kankanhalli,et al.  Print signatures for document authentication , 2003, CCS '03.

[9]  Jan P. Allebach,et al.  Signature-embedding in printed documents for security and forensic applications , 2004, IS&T/SPIE Electronic Imaging.

[10]  Jan P. Allebach,et al.  A survey of forensic characterization methods for physical devices , 2006, Digit. Investig..

[11]  Orhan Bulan,et al.  Geometric distortion signatures for printer identification , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.