Ali Toqeer, Jan Salman, Alkhodre Ahmad, Nauman Mohammad, Amin Muhammad, Siddiqui Muhammad Shoaib
Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah, Saudi Arabia.
Malaysian Institute of Information Technology, University Kuala Lumpur, Kuala Lumpur, Malaysia.
PeerJ Comput Sci. 2019 Sep 2;5:e216. doi: 10.7717/peerj-cs.216. eCollection 2019.
Conventional paper currency and modern electronic currency are two important modes of transactions. In several parts of the world, conventional methodology has clear precedence over its electronic counterpart. However, the identification of forged currency paper notes is now becoming an increasingly crucial problem because of the new and improved tactics employed by counterfeiters. In this paper, a machine assisted system-dubbed DeepMoney-is proposed which has been developed to discriminate fake notes from genuine ones. For this purpose, state-of-the-art models of machine learning called Generative Adversarial Networks (GANs) are employed. GANs use unsupervised learning to train a model that can then be used to perform supervised predictions. This flexibility provides the best of both worlds by allowing unlabelled data to be trained on whilst still making concrete predictions. This technique was applied to Pakistani banknotes. State-of-the-art image processing and feature recognition techniques were used to design the overall approach of a valid input. Augmented samples of images were used in the experiments which show that a high-precision machine can be developed to recognize genuine paper money. An accuracy of 80% has been achieved. The code is available as an open source to allow others to reproduce and build upon the efforts already made.
传统纸币和现代电子货币是两种重要的交易模式。在世界上的几个地区，传统方法比其电子对应方法具有明显的优先权。然而，由于造假者采用了新的和改进的手段，识别伪造纸币现在正成为一个日益关键的问题。在本文中，提出了一种机器辅助系统——称为DeepMoney，它被开发用于区分假钞和真钞。为此，采用了称为生成对抗网络（GANs）的机器学习的先进模型。GANs使用无监督学习来训练一个模型，然后该模型可用于进行有监督的预测。这种灵活性通过允许对未标记数据进行训练，同时仍能做出具体预测，提供了两全其美的效果。该技术被应用于巴基斯坦纸币。使用了先进的图像处理和特征识别技术来设计有效输入的整体方法。实验中使用了增强的图像样本，结果表明可以开发出高精度的机器来识别真钞。已经实现了80%的准确率。代码作为开源提供，以便其他人能够重现并在已取得的成果基础上进行改进。
