A Novel Approach for Detection of Counterfeit Indian Currency Notes Using Deep Convolutional Neural Network

Kumar, Sunil; Singal, Gaurav; Sirikonda, Shwetha; Nethravathi, R.

doi:10.1088/1757-899x/981/2/022018

Cited by 22 publications

(6 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Artificial intelligence (AI) recently has advanced unprecedentedly with the introduction of some foundation models that were proven to be effective in various problem domains [2][3][4][5]. Some AI technologies have been employed for small-scale counterfeit examining systems [6][7][8]. However, the proposed neural networks still have several limitations in building an automated system for examining counterfeit copies at a large scale.…”

Section: Of 18mentioning

confidence: 99%

“…Illegal replicas are spreading rapidly due to technological advances in logistics. Accordingly, various methods have been proposed for detecting such counterfeiting through various methods [6][7][8]11]. Among them, a way to automatically calculate the probability that a product is a counterfeit based on online customer reviews in the market has been proposed [11].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

ENN: Hierarchical Image Classification Ensemble Neural Network for Large-Scale Automated Detection of Potential Design Infringement

Lee,

Jeong,

Yoon

2023

Preprint

View full text Add to dashboard Cite

This paper presents a two-stage hierarchical neural network using image classification and object detection algorithms as key building blocks for a system that automatically detects a potential design right infringement. This neural network is trained to return the Top-N original design right records that highly resemble the input image of a counterfeit. Design rights specify the unique aesthetic characteristics of a product. Due to the rapid change of trends, new design rights are continuously generated. This work proposes an Ensemble Neural Network (ENN), an artificial neural network model that aims to deal with a large amount of counterfeit data and design right records that are frequently added and deleted. At first, we performed image classification and objection detection learning per design right using the existing models with a proven track record of high accuracy. The distributed models form the backbone of the ENN and yield intermediate results aggregated at a master neural network. This master neural network is a deep residual network paired with a fully connected network. This ensemble layer is trained to determine the sub-models that return the best result for a given input image of a product. In the final stage, the ENN model multiples the inferred similarity coefficients to the weighted input vectors produced by the individual sub-models to assess the similarity between the test input image and the existing product design rights to see any sign of violation. Given 84 design rights and the sample product images taken meticulously under various conditions, our ENN model achieved average Top-1 and Top-3 accuracies of 98.409% and 99.460%, respectively. Upon introducing new design rights data, a partial update of the inference model was done an order of magnitude faster than the single model. ENN maintained a high level of accuracy as it scaled out to handle more design rights. Therefore, the ENN model is expected to offer practical help to the inspectors in the field, such as the customs at the border that deal with a swarm of products.

show abstract

Section: Of 18mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

ENN: Hierarchical Image Classification Ensemble Neural Network for Large-Scale Automated Detection of Potential Design Infringement

Lee,

Jeong,

Yoon

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Illegal replicas are spreading rapidly due to technological advances in logistics. Accordingly, various methods have been proposed for detecting such counterfeiting [6][7][8]11]. Among them, a way to automatically calculate the probability that a product is a counterfeit based on online customer reviews in the market has been proposed [11].…”

Section: Related Workmentioning

confidence: 99%

“…Artificial Intelligence (AI) has recently advanced unprecedentedly with the introduction of some foundation models that have been proven to be effective in various problem domains [2][3][4][5]. Some AI technologies have been employed for small-scale counterfeit examining systems [6][7][8]. However, the proposed neural networks still have several limitations when building an automated system for examining counterfeit copies at a large scale.…”

Section: Introductionmentioning

confidence: 99%

ENN: Hierarchical Image Classification Ensemble Neural Network for Large-Scale Automated Detection of Potential Design Infringement

Lee,

Jeong,

Yoon

2023

Applied Sciences

View full text Add to dashboard Cite

This paper presents a two-stage hierarchical neural network using image classification and object detection algorithms as key building blocks for a system that automatically detects a potential design right infringement. This neural network is trained to return the Top-N original design right records that highly resemble the input image of a counterfeit. This work proposes an ensemble neural network (ENN), an artificial neural network model that aims to deal with a large amount of counterfeit data and design right records that are frequently added and deleted. First, we performed image classification and objection detection learning per design right using acclaimed existing models with high accuracy. The distributed models form the backbone of the ENN and yield intermediate results aggregated at a master neural network. This master neural network is a deep residual network paired with a fully connected network. This ensemble layer is trained to determine the sub-models that return the best result for a given input image of a product. In the final stage, the ENN model multiplies the inferred similarity coefficients to the weighted input vectors produced by the individual sub-models to assess the similarity between the test input image and the existing product design rights to see any sign of violation. Given 84 design rights and the sample product images taken meticulously under various conditions, our ENN model achieved average Top-1 and Top-3 accuracies of 98.409% and 99.460%, respectively. Upon introducing new design rights data, a partial update of the inference model was performed an order of magnitude faster than the single model. The ENN maintained a high level of accuracy as it was scaled out to handle more design rights. Therefore, the ENN model is expected to offer practical help to the inspectors in the field, such as customs at the border that deal with a swarm of products.

show abstract

“…CNN is a generally prominent technique utilized for the purposes of image detection and recognition [6], [7]. Faster R-CNN has been utilized for object detection in related literature because of its high level of accuracy and its ability to automatically extract features.…”

Section: Introductionmentioning

confidence: 99%

Fake Currency Detection using Modified Faster Region-Based Convolutional Neural Network

Ibitoye

2024

International Journal of Electrical Engineering and Computer Science

View full text Add to dashboard Cite

Significant technological advancements in the printing and scanning industries exacerbated the counterfeiting problem. As a consequence, counterfeit currency has an impact on the economy and diminishes the value of genuine currency. Therefore, it is essential to detect the counterfeit currency. The majority of previous methods rely on hardware and image processing techniques. Using these methods to detect counterfeit currency is inefficient and time-consuming. We have proposed a system for the detection of counterfeit currency using a modified faster region-based convolution neural network (Faster R-CNN) to circumvent the aforementioned issue. This study identifies counterfeit currency by analyzing images of currency. One thousand images of currency note are used as dataset to train a Faster-RCNN model on inception V2 architecture to learn the feature map of currencies. Upon successful training and validation of the model, 500 images of counterfeit currencies were used to test the model. The proposed method efficiently identifies 96% of counterfeit currency images tested. Other evaluation means such as mean average precision and detection accuracy show that the developed system has an accuracy of 97%.

show abstract

A Novel Approach for Detection of Counterfeit Indian Currency Notes Using Deep Convolutional Neural Network

Cited by 22 publications

References 10 publications

ENN: Hierarchical Image Classification Ensemble Neural Network for Large-Scale Automated Detection of Potential Design Infringement

ENN: Hierarchical Image Classification Ensemble Neural Network for Large-Scale Automated Detection of Potential Design Infringement

ENN: Hierarchical Image Classification Ensemble Neural Network for Large-Scale Automated Detection of Potential Design Infringement

Fake Currency Detection using Modified Faster Region-Based Convolutional Neural Network

Contact Info

Product

Resources

About