Discrimination of original and fake cash notes through Generative Adversarial Networks
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GAN's essentially consist of two parts as the name suggests: generator and discriminator. The role of generator is to generate similar ones as the original and feed it to the discriminator whereas the role of discriminator is to discriminate the original and fake ones viz a classification problem. ...learn more
Project status: Concept
Groups
Student Developers for AI
Intel Technologies
AI DevCloud / Xeon,
Intel Opt ML/DL Framework,
Intel Python,
OpenVINO
Overview / Usage
The project collects dataset which contains cash notes of all values. Generator generates fake notes which look very similar to original notes. So, the original and fake notes are fed to discriminator in equal amounts so that it learns and discriminates the notes. Since it is a classification problem, the output will be 0 or 1 (original note=1 and fake note=0)
problem solved:
The main issue that would be solved is people who use fake notes can be caught similar to those who use fake paintings to make money.
Technologies Used
Technology used: Pytorch