Abstract
Source printer identification for printed documents has been studied extensively in recent years. Applying machine learning to features extracted from the artifacts of the printed papers is a potential approach in this field. Due to the fact that extracting features is a manual task that requires domain knowledge from the expert, which is one of the most resource-intensive tasks, In this work, we aim to reduce the number of training features on many different machine learning models but guarantee the high performance of the identifying results. Following the work of the authors from [1], our proposed features selection methods show that we can achieve about the same accuracy while significantly reduced the number of features.
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Acknowledgements
We would like to thank Dr. Quoc Thong Nguyen at Université Bretagne Sud for his contribution on the data. This paper is supported by a project with the International University, Ho Chi Minh City, Vietnam (contract No. T2020–04-IT/HĐ-ĐHQT-QLKH, dated 01/02/2021).
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Nguyen, Q.P., Dang, N.T., Mai, A., Nguyen, V.S. (2021). Features Selection in Microscopic Printing Analysis for Source Printer Identification with Machine Learning. In: Dang, T.K., Küng, J., Chung, T.M., Takizawa, M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2021. Communications in Computer and Information Science, vol 1500. Springer, Singapore. https://doi.org/10.1007/978-981-16-8062-5_14
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DOI: https://doi.org/10.1007/978-981-16-8062-5_14
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