The proliferation of e-commerce platforms has led to a surge in counterfeit products, with fraudulent sellers exploiting brand logos to deceive consumers and bypass verification systems. This misuse of intellectual property poses financial risks and damages brand reputation. The Online Fake Logo Detection System is an intelligent automated solution designed to classify brand logos as authentic or counterfeit using advanced machine learning and image processing techniques. Leveraging a Convolutional Neural Network (CNN) trained on a comprehensive Kaggle logo dataset, the system captures intricate visual patterns, color distributions, spatial features, and structural characteristics that distinguish genuine logos from fraudulent ones.The detection pipeline consists of five stages: image acquisition and preprocessing, feature extraction, model training and optimization, real-time logo classification, and result visualization with confidence scoring. Preprocessing employs OpenCV for resizing, noise reduction, histogram equalization, and normalization. The CNN architecture integrates convolutional layers, pooling, dropout regularization, and dense layers optimized with the Adam optimizer.
Loke et al. (Thu,) studied this question.