Mobilenet V2-FCD: Fake Currency Note Detection

Author(s):  
Tejaswi Potluri ◽  
Somavarapu Jahnavi ◽  
Ravikanth Motupalli
Keyword(s):  
2021 ◽  
Vol 2089 (1) ◽  
pp. 012008
Author(s):  
B Padmaja ◽  
P Naga Shyam Bhargav ◽  
H Ganga Sagar ◽  
B Diwakar Nayak ◽  
M Bhushan Rao

Abstract Visually impaired and senior citizens find it difficult to identify different banknotes, driving the need for an automated system to recognize currency notes. This study proposes recognizing Indian currency notes of various denominations using Deep Learning through the CNN model. While not recognizing currency notes is one issue, identifying fake notes is another major issue. Currency counterfeiting is the illegal imitation of currency to deceive its recipient. The current existing methodologies for identifying a phony note rely on hardware. A method completely devoid of hardware that relies on specific security features to help distinguish a legitimate currency note from an illegitimate one is much needed. These features are extracted using the boundary box region of interest (ROI) and Canny Edge detection in OpenCV implemented in Python, and the multi scale template matching algorithm is applied to match the security features and differentiate fake notes from legitimate notes.


Author(s):  
S.A. SivaKumar ◽  
R. Naveen ◽  
Dharmesh Dhabliya ◽  
B. Maruthi Shankar ◽  
B. Naga Rajesh
Keyword(s):  

2016 ◽  
Vol 23 (3) ◽  
pp. 542-558
Author(s):  
Balasubramaniyan Viswanathan

Purpose The purpose of this paper is to study the counterfeit currency network in India. This research is an endeavour to bring out various layers which act as source, collection and distribution points in a counterfeit currency network in India. This paper also deals with the fake currency network and its linkages to terrorism. Design/methodology/approach Methodology adopted is a descriptive one which conducts a content analysis on materials derived from secondary sources supported by information from primary source data acquired through the Right to Information Act. Findings This paper argues that the existing measure of calculating the incidence of counterfeit notes per million is understated by the relevant stakeholders in India. This measure changes drastically when other factors such as high denomination notes and police seizures are taken into account, which has not been attempted, though it is duly acknowledged by the stakeholders. This paper has attempted to map the locations in India which act as ingress, distribution and circulation points based on evidentiary data derived from the seizure records. This paper also highlights the fact that criminal gang-operated networks of fake currency are compartmentalised, while the networks operated by terror groups are de-compartmentalised. Practical implications In the process, this paper attempts to enlighten stakeholders like law enforcement agencies, banking regulators and counter terrorism community on the penetration levels of the fake Indian currency note (FICN) networks in India and the need to target these important nodes or points or layers to break up the FICN network. This also highlights fund-raising mechanisms of terror groups, where FICN acts as the main funding resource for groups like the Indian Mujahideen for carrying out low-cost terror attacks. Originality/value The key findings of this research lie in its originality of presentation of facts in a systematic fashion.


Author(s):  
Jeff Bekomo Iteku ◽  
Donel Moswala Likabo ◽  
Aaron Lelo Pambu ◽  
Gédéon Ngiala Bongo ◽  
Ruth Katunda ◽  
...  

Aims: The incidence of infectious diseases is still a vital concern in developing countries. Recently, hygienists have focused on the risk of transmitted diseases through currency notes. This study aims at the determination of potential pathogenic microorganisms found on the Congolese Francs currency notes circulating in Kinshasa, Democratic Republic of the Congo. Place and Duration of the Study: This study was carried out in Kinshasa city, in the Democratic Republic of the Congo between September 3 and 29, 2019 at the Bacteriology Laboratory of the National Institute for Biomedical Research. Methods: During this study, 36 currency notes of different denominations have been used for microbiological analysis. Currency notes were collected from vendors of the Central market and currency note dealers at Kintambo Magasin market in Kinshasa. The identification of microorganisms (bacteria and fungi) was performed using gram staining and biochemical analyses. Results: The findings reveal the presence of following microorganisms, namely Bacillus spp, Staphylococcus aureus, Staphylococcus spp., Enterobacter spp, Escherichia coli, Serratia spp, Citrobacter spp, Salmonella enteritidis as well as molds on the Congolese currency notes. It should be observed that circulated currency notes in Kinshasa are contaminated by bacteria and fungi. The contamination rate was based on the fact, that money is new, clean or dirty. Conclusion: Congolese currency notes constitute the potential sources of infectious disease transmission if hygienic conditions are not respected. Molecular studies are required in order to determine the antibiotic resistance gene of these microorganisms. As the population does not know how to store these notes properly, their contamination would eventually become a major public health hazard. Therefore, a need of an awareness of the population in order to apply hygienic rules while handling currency notes. This is for the first time that such a study is being carried out in the Democratic Republic of the Congo.


Fake Currency Detection is the biggest problem faced by many countries including India. The advancement of colour printing technology has increased the rate of fake currency note printing and duplicating the notes on a very large scale. So, it has become a necessity to develop a tool that detects the fake currency note in a less time and in a more efficient manner using Image processing.


The counterfeit currency printing rate has been increased with the progress of color printing Technology. Some people are printing fake currency using some laser printers. Therefore, the counterfeit currency notes production instead of the original currency notes has been rapidly increasing. This requires an efficient system that identifies the counterfeit currency note and displays the result. This paper developed a system consisting of image preprocessing, gray-scale conversion, image segmentation, edge detection, feature extraction, and comparison modules. The currency note is scanned and the scanned image is used in the modules. The outcome of the system will foretell if the note is counterfeit or genuine


2019 ◽  
Vol 4 (1) ◽  
pp. 26-30 ◽  
Author(s):  
Hassan Mohammad Tawfeeq ◽  
Mohammed Hassan Fatah ◽  
Ahmed Mohammed Tofiq

Every day new sources of microbial and especially bacterial infections are reported, which are not taken into account, the fact that these sources have been implicated in the outbreaks of these infections. The goals of the current research focused on the exploration of the scope of microbial pollution of the widely traded paper currency notes of the Iraqi currencies (250, 1000 and 5000 Iraqi Dinars) in Kalar city. 300 banknotes (100 samples for each of the denominations under investigation) in circulation were gathered from different categories of Kalar population and one fresh sample for each of the three currencies (control negative). Each bill was rinsed in 5 ml D.W then cultured on Nutrient agar, Mannitol Salt agar and MacConkey agar, respectively, then incubated at 37 ºC for up to 48 hours. Results revealed the rate of microbial contamination, specifically bacterial ones as 94%, 68%, and 60%, respectively on the denominations of 250, 1000, and 5000 of Iraqi Dinars. In a descending order, and with regard to the prevalence rate, bacterial species contaminated 250, 1000 and 5000 Iraqi denominations respectively were as follows: 55.31%, 32.35% and 36.66% for Bacillus sp.; 14.89%, 38.23% and 20% for coagulase-negative Staphylococcus; 8.51%, 8.82% and 20% for E. coli; 2.12%, 11.76 and 20% for Pseudomonas sp.; 2.12% for each of Klebsiella sp., and Salmonella sp.; 10.63% and 4.25% for Enterobacter sp. and Staphylococcus aureus, respectively, and only on 250 denomination;  2.94% and 5.88% for each of Corynebacterium sp. and Serratia sp. only on 1000 currency note; lastly, 3.33% for Proteus sp. on 5000 IQD only. These outcomes suggest that bacterial contamination of Iraqi Dinars, specifically the 250 denomination is risky and could be regarded as the real source of infectious diseases with the most dangerous pathogenic bacteria.  


One of the leading causes of economic instability is the large-scale counterfeiting of the paper currency notes. Several media reports bring to light the alarming cases and the humungous scales of currency counterfeiting and how this issue has become very serious now. A report on how the Government is coping with these threats with new and stricter rules however counterfeiters adapt to the new rules in an alarmingly fast pace. Criminals continue to find a loophole in the system despite such strict security features. There have been impressive discoveries in the field of counterfeit currency, and this coupled with new age digital technology, counterfeiting is being fought well. However, it is impossible to track all counterfeit notes and impossible to have them checked at a short amount of time. Existing systems involve filing a case with the police, sending the documents for verification and waiting for the results to come. This method is based on Deep Learning, which has seen tremendous success in image classification tasks in recent times. This technique can help both people and machine in identifying a fake currency note in real time through an image of the same. Traditional Deep Learning algorithms require tremendous amount of compute power and storage and hence it is an expensive and elaborate process. The main goal is to make a faster and simpler mechanism to detect a counterfeit note that can be implemented in any random place like an ATM dispenser or an android application. The success of this application will greatly help the quick identification of the threat and help law enforcement in finding the source of the threat faster.


2020 ◽  
Vol 32 ◽  
pp. 03047
Author(s):  
Anushka Kulkarni ◽  
Prachi Kedar ◽  
Aishwarya Pupala ◽  
Priyanka Shingane

Currency is used to carry out not only business but also for various other transactions to get access to various services and commodities. There are a total of 7 denominations for the Indian currency each with unique features to distinguish them from each other and with various and distinct security features to prevent them from fraudulent copying. However,with the evolution of technology, there is also an increase in the ways in which fake forms of these currencies are created. These fake or counterfeit notes have various ill-effect on society. The proposed system will be used to check the genuine Indian currency notes and to find the denomination of the currency note. Comparative study for various image processing algorithms was conducted to identify and select the one which will be able to extract more prominent features,and is also better in terms of processing time,outlier rejection, efficiency in computation and in feature matching. After which a real time system is created for currency detection in real time.


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