CNN-Based Fake Brand Logo Recognition and Authentication

Abstract

Counterfeit logos have become a major concern for businesses as they can damage brand reputation and mislead customers. This project presents a deep learning-based approach for detecting fake and original logos using a Convolutional Neural Network (CNN). A dataset containing genuine and fake logo images is collected and pre-processed through image resizing, normalization, and data shuffling to improve the quality of training data. The processed dataset is divided into training and testing sets in an 80:20 ratio. The CNN model automatically learns important visual features from the images and classifies logos with high accuracy. The developed system provides a simple graphical user interface that allows users to upload logo images and instantly verify their authenticity. Experimental results show that the proposed model achieves reliable classification performance while reducing manual verification effort. This system can support organizations in identifying counterfeit logos and protecting brand identity through an efficient and automated detection process.