Features Selection in Microscopic Printing Analysis for Source Printer Identification with Machine Learning-Reference-Cited by-同舟云学术

Features Selection in Microscopic Printing Analysis for Source Printer Identification with Machine Learning

Published:2021 Issue: Volume: Page:210-223
ISSN:1865-0929
Container-title:Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications
language:
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Author:

Nguyen Q. Phu,Dang Nhan Tam,Mai An,Nguyen Van Sinh

Publisher

Springer Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-16-8062-5_14

Reference15 articles.

1. Nguyen, Q.T., Mai, A., Chagas, L., Reverdy-Bruas, N.: Microscopic printing analysis and application for classification of source printer. Comput. Secur. 108, 102320 (2021). https://doi.org/10.1016/j.cose.2021.102320

2. International Data Corporation. (2021, August 3) IDC Forecasts Worldwide Page Volumes to Rebound In 2021, But Will Not Reach Pre-COVID-19 Levels. https://www.idc.com/getdoc.jsp?containerId=prUS48126321

3. Chiang, P.J., et al.: Printer and scanner forensics. IEEE Signal Process. Mag. 26(2), 72–83 (2009)

4. Ferreira, A., Navarro, L.C., Pinheiro, G., dos Santos, J.A., Rocha, A.: Laser printer attribution: exploring new features and beyond. Forensic Sci. Int. 247, 105–125 (2015)

5. Khanna, N., Delp, E.J.: Intrinsic signatures for scanned documents forensics: effect of font shape and size. In: Proceedings of 2010 IEEE International Symposium on Circuits and Systems, pp. 3060–3063. IEEE (May 2010)

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