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

机译:使用基于CNN的深度学习来检测伪造账单及其伪造设备

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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.
机译:由于扫描和印刷技术的进步,伪造的账单很容易锻造。个人不太可能找到伪造的账单。本文提出了一种深入的学习算法来检测伪造账单及其伪造设备。所提出的算法采用由2个卷积层和2个完全连接的层组成的卷积神经网络模型。在卷积层中,施加整流的线性单元和最大池。在完全连接的图层中,省略掉落。为了展示算法的性能,使用原始账单和伪造账单进行实验,并使用不同的制造商打印机。已经实现了近100%的检测精度。

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