Real Time Fake Note Detection using Deep Convolutional Neural Network
Mangesh Manikrao Ghonge, Tejas Kachare, Manisha Sinha, Siddharth Kakade, Siddharth Nigade, Sandip Shinde · 2022 Second International Conference on Computer Science, Engineering and Applications (ICCSEA) · 2022
Nowadays printing fake notes has become very easy due to the availability of high-tech printing machines and advancements in color printing technology. Notes can be printed with maximum accuracy without any slip mistake so detecting fake notes in today’s world is almost unattainable. Hence the fake notes in the market have reduced the value of the original currency. This counterfeit negatively affects nations’ wealth. So, today it is necessary to detect fake currency. All the currently available methods are based on image processing techniques. Finding fake currencies with these methods is less efficient, unreliable, and time-consuming. The proposed work is based on the deep convolutional neural network used to detect counterfeit currency through the mobile application. The self-generated dataset is used to train the model. Further, it was tested using real-time images captured through the smartphone camera. The training and validation accuracy is 96.66%, while the testing accuracy is 86.65%.