Abstract
This study investigates the applicability of the Auto Machine Learning-based approach (AutoML) for analyzing microscopic printed document images to attribute that document to its source printer. In this perspective, AutoML, a new rising star of machine learning in practice, has shone brightly as it can satisfy the demand of Machine Learning practitioner communities. In this work, three candidates from popular Machine Learning models and two representatives from AutoML are nominated for a competition. The challenges of traditional methods and the merits of applying AutoML are highlighted through the experiments. Especially the power of ensemble methods to achieve the best possible model for our experimental dataset. Furthermore, the learnability of AutoML to the different levels of uncertainties of printed patterns is also recognized.
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Acknowledgement
This research is supported by a project with the International University, Ho Chi Minh City, Vietnam (contract No. T2020-01-IT/HĐ-ĐHQT-QLKH, dated 01/02/2021).
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Vo, PQ. et al. (2022). Auto Machine Learning-Based Approach for Source Printer Identification. In: Szczerbicki, E., Wojtkiewicz, K., Nguyen, S.V., Pietranik, M., Krótkiewicz, M. (eds) Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2022. Communications in Computer and Information Science, vol 1716. Springer, Singapore. https://doi.org/10.1007/978-981-19-8234-7_52
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