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Detecting Counterfeit Bills and Their Forgery Devices Using CNN-based Deep Learning

Authors:
Soo-Hyeon Lee
Hae-Yeoun Lee

Keywords: counterfeit bill detection; forgery device detection; deep learning; convolutional neural network.

Abstract:
Counterfeit bills are easy to forge due to the advances in scanning and printing technologies. Individuals are less likely to find counterfeit bills. This paper proposes a deep learning-based algorithm to detect counterfeit bills and their forgery devices. The proposed algorithm has adopted a convolutional neural network model composed of 2 convolutional layers and 2 fully connected layers. In the convolutional layers, rectified linear unit and max-pooling are applied. In the fully connected layers, drop out is applied. To show the performance of the algorithm, experiments are performed using original bills and counterfeit bills forged with different manufacturers' printers. Nearly 100% detection accuracy has been achieved.

Pages: 16 to 20

Copyright: Copyright (c) IARIA, 2018

Publication date: June 24, 2018

Published in: conference

ISSN: 2308-4529

ISBN: 978-1-61208-641-5

Location: Venice, Italy

Dates: from June 24, 2018 to June 28, 2018