• PRERANA PRATAP RANDHAVE
• YASH PRABHAKAR PATIL
• RITESH MADHAV PATE
• ABHISHEK PRAKASH PATIL
• Mr. SHRIKANT SHINDE

The widespread presence of counterfeit logos presents a significant challenge to brand integrity, consumer trust, and legal compliance. This research proposes an intelligent system to detect fake logos by leveraging advanced image processing and deep learning techniques, such as Convolutional Neural Networks (CNN). The system processes input images by segmenting them into identifiable components and extracting key features for classification. By comparing these features with a verified database of authentic logos, the model efficiently differentiates between genuine and counterfeit logos. This approach addresses the limitations of manual detection processes, providing scalability and improved accuracy in real-time applications. The system's potential extends to safeguarding intellectual property, supporting legal enforcement, and enhancing consumer protection across various sectors, including e-commerce and advertising. The study also explores challenges like adversarial attacks and computational efficiency, proposing solutions to ensure robust and adaptive detection in dynamic environments.

Image processing, Deep learning, Convolutional Neural Networks, E-commerce.

"ALGORITHMS USED FOR FAKE LOGO DETECTION: A REVIEW", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 2, page no.b690-b693, February-2025, Available :http://www.jetir.org/papers/JETIR2502169.pdf

"ALGORITHMS USED FOR FAKE LOGO DETECTION: A REVIEW", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 2, page no. ppb690-b693, February-2025, Available at : http://www.jetir.org/papers/JETIR2502169.pdf