Banknote Counterfeit Detection through Background Texture Printing Analysis

Berenguel, Albert; Terrades, Oriol Ramos; Lladós, Josep; Cañero, C.

doi:10.1109/das.2016.34

Cited by 17 publications

(14 citation statements)

References 9 publications

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“…Python has also been used to develop and implement a framework for the identification of Pakistani currency. Berenguel et al (2016) also worked on methods by which to identify genuine and photocopied bank notes. The technique applied was to differentiate the texture between the original and photocopied notes.…”

Section: Related Workmentioning

confidence: 99%

DeepMoney: counterfeit money detection using generative adversarial networks

Syed

Jan

Alkhodre

et al. 2019

PeerJ Computer Science

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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.

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Section: Related Workmentioning

confidence: 99%

DeepMoney: counterfeit money detection using generative adversarial networks

Syed

Jan

Alkhodre

et al. 2019

PeerJ Computer Science

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“…The usage of most common and simple Adam optimizer and ReLU activation the proposed approach reached its apex at 55% accuracy. To secure a dataset with a resolution of 1200dpi expensive scanners and many cameras were employed by [7]. The approach narrowly focuses on just the intaglio printing which are manually cropped.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The evaluation of results has been depicted below by the confusion matrices in Table IV and V, and column graph with test accuracy against epochs where columns represent accuracy when dropout was included and excluded respectively ( Fig 4.1 ). The performance of proposed approach is evaluated against accuracy (6) and sensitivity (7) with obtained values 85%, 91% for test dataset and 87%, 92% for train dataset respectively ( Fig 4.2 ).…”

Section: Fig 34 -Flow Diagram For Training and Testingmentioning

confidence: 99%

Smartphone enabled Counterfeit Note Detection using Siamese Network

2019

IJITEE

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Counterfeit note has a disastrous impact on a country’s economy. The circulation of such fake notes not only diminishes the value of genuine note but also results in inflation. The feasible solution to this burning issue is to create awareness about the counterfeit notes among public and to equip them with a technology to detect fake notes on their own. Though there exist numerous research articles on detection of fake notes, they are not handy. The reason for this could be the unavailability or unaffordability in acquiring the equipment for the same. This paper proposes an approach whose implementation can easily be deployed on a smart phone and hence anyone with access to them can use the application to detect the fake notes. The proposed approach consists of the processing phases including image procurement, pre-processing, data augmentation, feature extraction and classification. ₹500 notes are considered for experimentation analysis. Out of 17 distinctive features, 3 such from the obverse side are considered to evaluate the genuineness of the note. Siamese neural network is employed to build a model for effective classification of the notes. The performance of the proposed approach is evaluated at 85% with respect to accuracy.

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“…1) Texture descriptor: Although any texture feature can be used to represent the textures, we have selected dense SIFT because it is generally very competitive, outperforming specialized texture descriptors [10], [14].…”

Section: Counterfeit Modulementioning

confidence: 99%

E-Counterfeit: A Mobile-Server Platform for Document Counterfeit Detection

Centeno

Terrades

Canet

et al. 2017

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)

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This paper presents a novel application to detect counterfeit identity documents forged by a scan-printing operation. Texture analysis approaches are proposed to extract validation features from security background that is usually printed in documents as IDs or banknotes. The main contribution of this work is the end-to-end mobile-server architecture, which provides a service for non-expert users and therefore can be used in several scenarios. The system also provides a crowdsourcing mode so labeled images can be gathered, generating databases for incremental training of the algorithms.

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Banknote Counterfeit Detection through Background Texture Printing Analysis

Cited by 17 publications

References 9 publications

DeepMoney: counterfeit money detection using generative adversarial networks

DeepMoney: counterfeit money detection using generative adversarial networks

Smartphone enabled Counterfeit Note Detection using Siamese Network

E-Counterfeit: A Mobile-Server Platform for Document Counterfeit Detection

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