Fake Logo Detection

Abstract

Counterfeit branding, especially in logo duplication, has become a global challenge with the rise of online marketplaces. Detecting fake logos is vital for protecting brand identity and ensuring customer trust. This paper presents a computer vision-based solution for fake logo detection using OpenCV and Python. The method uses image preprocessing, ORB (Oriented FAST and Rotated BRIEF) for feature extraction, and FLANN (Fast Library for Approximate Nearest Neighbors) for efficient matching. Experimental analysis on a custom dataset of real and fake logos demonstrates the system’s effectiveness and speed, proving it suitable for real-time and scalable deployment.