Detect Genuine And Counterfeit Logos Using A Cnn With The Inception V3 Pre Trained Model To Achieve High accuracy

Abstract

The Proliferation Of Counterfeit Products Poses Significant Challenges To Brand Integrity And Consumer Trust. This Paper Presents A Comprehensive Survey On Detecting Genuine And Counterfeit Logos Using Convolutional Neural Networks (CNNs) With The Inception-V3 Pre-trained Model. We Review Recent Advancements In Deep Learning-based Logo Detection, Focusing On Accuracy, Robustness, And Computational Efficiency. The Survey Highlights Key Methodologies, Datasets, Performance Metrics, And Challenges In This Domain. Our Analysis Demonstrates That Inception-V3, Combined With Fine-tuning And Data Augmentation, Achieves State-of-the-art Performance In Distinguishing Authentic And Counterfeit Logos. Future Research Directions Include Improving Generalization Across Diverse Logo Designs And Integrating Explainable AI Techniques For Enhanced Interpretability.

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