SpotTheFake: An Initial Report on a New CNN-Enhanced Platform for Counterfeit Goods Detection
It addresses the problem of counterfeit goods trade, which accounts for over 3.3% of world trade, for society, but appears incremental in method.
The paper tackles counterfeit goods detection by developing a platform using VGG16 with transfer learning and a multi-stage procedure, achieving reliable and robust results in initial experiments on a custom image dataset.
The counterfeit goods trade represents nowadays more than 3.3% of the whole world trade and thus it's a problem that needs now more than ever a lot of attention and a reliable solution that would reduce the negative impact it has over the modern society. This paper presents the design and early stage development of a novel counterfeit goods detection platform that makes use of the outstsanding learning capabilities of the classical VGG16 convolutional model trained through the process of "transfer learning" and a multi-stage fake detection procedure that proved to be not only reliable but also very robust in the experiments we have conducted so far using an image dataset of various goods which we gathered ourselves.