Logo authentication plays a crucial role in combating counterfeiting and preserving brand integrity. This project focuses on classifying real and fake logos using advanced deep learning techniques. A custom image dataset consisting of real and counterfeit logos is used to train and evaluate classification models. Two state-of-the-art approaches are employed: Convolutional Neural Networks (CNNs), known for their efficiency in extracting spatial features from images, and Vision Transformers (ViTs), which leverage self-attention mechanisms for capturing long-range dependencies in visual data. The CNN model focuses on localized patterns and textures to distinguish genuine logos, while the ViT model learns global image representations for robust classification. By comparing the performance of both models in terms of accuracy, precision, and computational efficiency, the study aims to identify the most effective approach for logo verification. The proposed system can be integrated into digital platforms to automatically detect counterfeit logos, supporting brand protection and enhancing consumer trust.

Keywords:
Fake Logo Detection, Real Logo Classification, Convolutional Neural Networks (CNN), Vision Transformers (ViT).