Fake Logo Detection

Abstract

Fake logos pose a significant challenge in various industries, particularly in the fashion and food sectors, leading to brand reputation damage and financial losses. This project focuses on developing an automated fake logo detection system using deep learning techniques. By leveraging Convolutional Neural Networks (CNNs) with transfer learning, specifically using a fine-tuned ResNet50 model, the system accurately differentiates between authentic and counterfeit logos.
The dataset consists of real and fake logos, primarily from food and fashion brands. Extensive image preprocessing, including resizing, normalization, and data augmentation, is applied to enhance model performance. The model's final layers are customized for binary classification, with adaptive learning rate adjustment using ReduceLROnPlateau and early stopping to prevent overfitting.
A notable feature of this project is its automatic model-saving mechanism, which stores the model once the validation accuracy reaches 95%, ensuring optimal performance without unnecessary training. After training, the model can predict real or fake logos using new images without requiring retraining, providing efficient realtime detection.
Evaluation metrics, including accuracy, precision, recall, and F1-score, are used to assess model performance. The results demonstrate the system's effectiveness in detecting counterfeit logos. The project has applications in brand protection, ecommerce platforms, and quality assurance to prevent the distribution of fake products.
Future enhancements could include expanding the
dataset to cover more brand categories and further optimizing model performance. This project exemplifies how deep learning can be applied to solve real-world challenges and ensure brand integrity.