DeepMoney: counterfeit money detection using generative adversarial networks
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2019-09-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-216.pdf |
| _version_ | 1819211013473435648 |
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| author | Toqeer Ali Salman Jan Ahmad Alkhodre Mohammad Nauman Muhammad Amin Muhammad Shoaib Siddiqui |
| author_facet | Toqeer Ali Salman Jan Ahmad Alkhodre Mohammad Nauman Muhammad Amin Muhammad Shoaib Siddiqui |
| author_sort | Toqeer Ali |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-23T06:20:19Z |
| format | Article |
| id | doaj.art-67bdaa82a817447ab3c2618e192d84b6 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-12-23T06:20:19Z |
| publishDate | 2019-09-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-67bdaa82a817447ab3c2618e192d84b62022-12-21T17:57:13ZengPeerJ Inc.PeerJ Computer Science2376-59922019-09-015e21610.7717/peerj-cs.216DeepMoney: counterfeit money detection using generative adversarial networksToqeer Ali0Salman Jan1Ahmad Alkhodre2Mohammad Nauman3Muhammad Amin4Muhammad Shoaib Siddiqui5Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah, Saudi ArabiaMalaysian Institute of Information Technology, University Kuala Lumpur, Kuala Lumpur, MalaysiaFaculty of Computer and Information Systems, Islamic University of Madinah, Madinah, Saudi ArabiaComputer Science, FAST-NUCES, Peshawar, PakistanComputer Science, FAST-NUCES, Peshawar, PakistanFaculty of Computer and Information Systems, Islamic University of Madinah, Madinah, Saudi ArabiaConventional 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.https://peerj.com/articles/cs-216.pdfDeep LearningCounterfeit MoneyGenerative Adversarial Networks |
| spellingShingle | Toqeer Ali Salman Jan Ahmad Alkhodre Mohammad Nauman Muhammad Amin Muhammad Shoaib Siddiqui DeepMoney: counterfeit money detection using generative adversarial networks PeerJ Computer Science Deep Learning Counterfeit Money Generative Adversarial Networks |
| title | DeepMoney: counterfeit money detection using generative adversarial networks |
| title_full | DeepMoney: counterfeit money detection using generative adversarial networks |
| title_fullStr | DeepMoney: counterfeit money detection using generative adversarial networks |
| title_full_unstemmed | DeepMoney: counterfeit money detection using generative adversarial networks |
| title_short | DeepMoney: counterfeit money detection using generative adversarial networks |
| title_sort | deepmoney counterfeit money detection using generative adversarial networks |
| topic | Deep Learning Counterfeit Money Generative Adversarial Networks |
| url | https://peerj.com/articles/cs-216.pdf |
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