Fake logos are increasingly being used by counterfeiters to deceive customers and damage brand reputation. Detecting and eliminating these counterfeit logos is critical to ensuring brand authenticity and consumer trust. This paper explores the development of a Fake Logo Detection System leveraging Artificial Intelligence (AI) and web scraping techniques. Web scraping is utilized to collect a comprehensive dataset of logos from online sources, while AI, particularly Convolutional Neural Networks (CNNs), is employed to analyze and detect inconsistencies in logo designs. The system identifies counterfeit logos by comparing their features, such as fonts, colors, and patterns, against a database of genuine logos. The proposed solution provides a scalable, automated method for protecting brands, monitoring social media, and securing e-commerce platforms from counterfeit products. This approach highlights the importance of integrating AI and web scraping to address real-world challenges in combating logo counterfeiting effectively.
Fake logo detection, counterfeit logo detection, AI in logo detection, web scraping techniques, brand protection
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