Identification of Fake Indian Currency using Convolutional Neural Network
IEEE BASE PAPER ABSTRACT:
The progression of shading printing innovation has expanded the rate of Fake currency copying notes on a large scale. Albeit electronic monetary exchanges are turning out to be more popular and the utilization of paper cash has been diminishing as of late, banknotes still remain in distribution attributable to their dependability and straight forwardness in use. Few years ago, the printing should be possible in a printing-houses, yet presently anybody can print a money paper with most extreme exactness utilizing a straightforward laser printer. As an outcome, the issue of duplicate currency rather than the authentic ones has been increases generally. India had reviled the problems like defilement and dark cash and fake of money notes is likewise a big issue to it. To handle this problem, a deep learning-based framework is proposed to identify the fake Indian currency. The MATLAB tool has been used to identify the fake currency. The outcome will classify whether the Indian currency note is Real or Fake.
OUR PROPOSED ABSTRACT:
A fake version of the original currency is referred to as counterfeiting. Therefore, the Indian government does not endorse counterfeit money. In India, only the RBI is in charge of printing currency. Once screened and released into the market, counterfeit banknotes present an issue that the RBI must address every year. The printing and scanning industries’ significant technological advancements caused the counterfeiting problem to worsen. Therefore, counterfeit money has an impact on the economy and lowers the value of real money. The requirement to identify fraudulent currency is therefore greatest. The majority of the earlier systems rely on hardware and image processing methods. These techniques are less effective and take more effort to find fake money. To overcome the above problem, we have proposed the Identification of Fake Indian Currency using Xception Architecture. Through analysis of the currency images, our technique detects counterfeit money. Indian currency data sets for the 2000 and 500 rupee notes are used to train the Xception Architecture to learn the feature map of the respective currencies. The network is prepared to recognise fraudulent currencies in real time after the feature map has been learned. The suggested method takes less time and effectively detects forgeries of the 2000 and 500 currencies. The training accuracy of our suggested model was 93.34%, and the validation accuracy was 97.00%.
PROJECT OUTPUT VIDEO:
ALGORITHM / MODEL USED:
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- Monitor : 15’’ LED
- Input Devices : Keyboard, Mouse
- Ram : 4 GB
- Operating system : Windows 10.
- Coding Language : Python 3.8
- Web Framework : Flask
Sumalatha, B. Jayanth Reddy, T. Venkat Ram Reddy, “Identification of Fake Indian Currency using Convolutional Neural Network”, 2022 6th International Conference on Computing Methodologies and Communication (ICCMC), IEEE Conference, 2022.
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