The fashion and footwear industries, where brand value and customer trust are paramount, are under constant threat from counterfeit products. This research presents a deep learning-based solution for detecting counterfeit Nike shoes using the YOLOv8 model for object detection. the dataset included four classes: “Nike Fake Air Force,” “Nike Fake Jordan 1,” “Nike Original Air Force,” and “Nike Original Jordan 1,” comprising a total of 3,860 images. These were split into training (70%), validation (20%), and testing (10%) sets. The pre-trained, medium-sized YOLOv8 model was used for detection and classification, yielding promising results. The model achieved a mAP of 95.0%, with precision and recall scores of 92.2% and 91.8%, respectively, on the validation set. The images were web scrapped with the Chrome extension called "Download All Images" and then manually filtered so that they would be relevant and of good quality. Each image was then manually labelled using the RoboFlow platform. Nevertheless, the model appears to be promising for implementation in combating counterfeit products, with a high potential accuracy and efficiency.

Counterfeit Product Detection Object Detection Deep Learning Nike YOLOv8