Soo-Hyeon Lee, Hae-Yeoun Lee

KIPS Transactions on Software and Data Engineering, Vol. 8, No. 1, pp. 37-44, Jan. 2019
https://doi.org/10.3745/KTSDE.2019.8.1.37,   PDF Download:
Keywords: Global Feature, Local Feature, Deep Learning, Printer Identification, Convolutional Neural Network
Abstract

With the advance of digital IT technology, the performance of the printing and scanning devices is improved and their price becomes cheaper. As a result, the public can easily access these devices for crimes such as forgery of official and private documents. Therefore, if we can identify which printing device is used to print the documents, it would help to narrow the investigation and identify suspects. In this paper, we propose a deep learning model for printer identification. A convolutional neural network model based on local features which is widely used for identification in recent is presented. Then, another model including a step to calculate global features and hence improving the convergence speed and accuracy is presented. Using 8 printer models, the performance of the presented models was compared with previous feature-based identification methods. Experimental results show that the presented model using local feature and global feature achieved 97.23% and 99.98% accuracy respectively, which is much better than other previous methods in accuracy.

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