DSpace Repository

Counterfeit arduino board detection using image processing

Show simple item record

dc.contributor.author Bavaram, K.
dc.contributor.author Kartheeswaran, T.
dc.contributor.author Edwin Linosh, N.
dc.date.accessioned 2021-03-26T06:59:22Z
dc.date.accessioned 2022-07-07T05:06:57Z
dc.date.available 2021-03-26T06:59:22Z
dc.date.available 2022-07-07T05:06:57Z
dc.date.issued 2020
dc.identifier.uri http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/2160
dc.description.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. en_US
dc.language.iso en en_US
dc.publisher University of Jaffna en_US
dc.subject Arduino en_US
dc.subject Counterfeit, Image Processing en_US
dc.subject Iot en_US
dc.subject Feature extraction en_US
dc.title Counterfeit arduino board detection using image processing en_US
dc.type Article en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record