Volume 2 - Issue 2, March - April 2026

The rapid expansion of digital platforms and e-commerce has significantly increased the circulation of counterfeit products, many of which rely on fake or manipulated logos to imitate well-known brands. This growing issue poses serious threats to brand reputation, consumer trust, and economic stability. Detecting fake logos manually is both time-consuming and prone to human error, highlighting the need for an automated and reliable solution. This study presents a comprehensive approach to fake logo detection using Python, integrating image processing and machine learning techniques to identify counterfeit logos with high accuracy. The proposed system utilizes computer vision methods to preprocess input images, including resizing, normalization, and noise reduction, ensuring consistent data quality. Feature extraction techniques such as edge detection, color analysis, and texture mapping are employed to capture distinguishing characteristics of logos. In addition, deep learning models, particularly Convolutional Neural Networks (CNNs), are implemented to automatically learn complex patterns and visual features from the dataset. The model is trained on a collection of genuine and fake logo images, enabling it to effectively classify new inputs. Experimental results demonstrate that the system achieves high accuracy and reliability in distinguishing authentic logos from counterfeit ones. The use of deep learning significantly enhances performance compared to traditional methods, especially in handling variations in size, orientation, and image quality. Despite challenges such as dataset limitations and computational requirements, the proposed approach proves to be efficient and scalable.

Kathir.E, Dr.Kalaivani.N,"Fake Logo Detection Using Phyton" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(2): Page(944-954) Mar-Apr 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.