Abstract:
Counterfeit electronic products may cause many issues like poor-selling services, poor quality,
poor technical support, and also health problems to the customers. Especially nowadays the
usage of inter- et of Things (IoT) was
increased a lot. In this research, we have focused on a commonly used IoT microcontroller board
called Arduino. Arduino is a prototyping device with a microcontroller that can build various
circuits of your own with dedicated ideas. Counterfeits are with some attributes like the same
brand logo, and sold with similar names but having some changes or spelling changes on the
brand name, but with the same manufacturing designs or patterns. These kinds of copying are
visible and can be identified normally, but only the expert who knows about the original products attributes can identify the counterfeit items perfectly. the method of finding a counterfeit product can be categorized into three types: electrical inspection, device
fingerprinting, and physical inspection. We have chosen the optical-based method using image processing techniques because its easier than other methods, least cost, and more efficient. Optically, Arduino Board has some original features and it can be assessed to find the originality
of the board. We found nine features to detect the counterfeit board. We used OCR, Line Canny,
Threshold, SIFT, Multiscale Matching K-means Clustering algorithms to extract the features
from the Arduino board images. The front view image of the board is enough to detect the
counterfeit board. We have devised five steps to find calculate the originality percentage. We
have used around 80 images for testing among that 30 were original, 30 were fake, and 20 non Arduino board images. Approximately 93 % of accuracy on original, 100% accuracy in fake,
and 100% in detecting non -Arduino boards. Our method is rule-based, simple, and efficient.
Further, this method will work for different versions of boards too. The project is comparatively
better and the future work is planned to implement a hardware system to detect the fake Arduino
boards using cameras and embedded devices.
