Abstract:
This research introduces an innovative approach to detect fake logos using Python, combining image analysis techniques and machine learning algorithms to safeguard brands from unauthorized and counterfeit usage. With the increasing prevalence of counterfeit products and digital manipulation tools, the proposed system aims to provide a robust solution for identifying fraudulent logos across various digital platforms.
The detection system utilizes computer vision techniques to extract distinctive features from logos, focusing on shape, color, and texture analysis. Additionally, deep learning models, specifically Convolutional Neural Networks (CNNs), are employed to learn complex patterns and variations in logo designs. The training dataset includes authentic and manipulated logo images, allowing the model to generalize and identify subtle alterations indicative of forgery.
Key functionalities of the system include real-time logo analysis, batch processing for large datasets, and integration with web scraping to identify instances of unauthorized logo usage across online platforms. The implementation also incorporates techniques to handle variations in lighting, orientation, and background, ensuring robustness against a wide range of manipulation attempts.
The research evaluates the system’s performance using a diverse set of logo samples, assessing its accuracy, precision, and recall. The open-source nature of the implementation encourages collaboration and further improvements within the community. This research contributes to the protection of brand identity and intellectual property rights in the digital era by providing an effective and adaptable tool for fake logo detection in Python.