Preventing Fraudulent Vehicle Listings: Digital ADS Detection

Abstract

Abstract: With the increasing popularity of online selling platforms for vehicles, the risk of fraudulent advertisements has also risen significantly. This research paper addresses the challenge of detecting and preventing digital fraud ads, particularly those related to vehicle listings, on typical online vehicle selling web portals. The proposed approach leverages advanced algorithms to identify and filter out fraudulent vehicle ads, including unregistered vehicles and unnecessary spam ads posted by sellers. One of the key techniques employed in this research is pattern matching, which involves comparing the details of posted vehicles with the records from Regional Transport Offices (RTOs) to determine their registration status. By analyzing the vehicle details provided in the ads and cross-referencing them with RTO data, the system can flag unregistered vehicles, thus mitigating the risk of fraudulent listings. Additionally, the study incorporates the use of the You Only Look Once (YOLO) algorithm for object detection in the images accompanying the ads. By applying YOLO, the system can accurately identify vehicles in the images and extract relevant information, such as make, model, and license plate details. This allows for further validation of the authenticity of the listings and helps in filtering out spam ads containing irrelevant or misleading images.