• D.PRABHAKARAN
• R.ANISHA

Fake logos can be used for various malicious purposes such as counterfeiting, phishing, and brand impersonation. The proliferation of counterfeit products in the market has resulted in the loss of revenue for the original manufacturers, as well as posing a serious threat to the consumers who purchase such products, as they might be of low quality and unsafe for use. The problem of identifying fake logos manually is time-consuming and often prone to errors. Hence, there is a need for an automated system that can detect and identify fake logos quickly and accurately. The objective of this project is to develop a Logo Counterfeit Detection and Intimation System using Region Proposal Network (RPN) Logo Detection and Convolutional Neural Network (CNN) for Logo Identification, which can help identify counterfeit logos and alert the relevant authorities. TrustLogo is a web-based system for detecting and identifying counterfeit logos using advanced computer vision techniques. It is designed to help companies and organizations protect their brands and intellectual property by quickly identifying fake logos and taking appropriate actions. RPN is an object detection algorithm that can detect logos in real-time with high accuracy, while CNN is a deep learning model that can recognize logos with high precision. TrustLogo aims to provide a reliable and efficient solution for detecting counterfeit logos, reducing the potential financial losses and reputational damage that can result from trademark infringement.

TRUSTLOGO: LOGO COUNTERFEIT DETECTION AND INTIMATION SYSTEM USING DEEP LEARNING

"TRUSTLOGO: LOGO COUNTERFEIT DETECTION AND INTIMATION SYSTEM USING DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 6, page no.h680-h687, June-2025, Available :http://www.jetir.org/papers/JETIR2506786.pdf

"TRUSTLOGO: LOGO COUNTERFEIT DETECTION AND INTIMATION SYSTEM USING DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 6, page no. pph680-h687, June-2025, Available at : http://www.jetir.org/papers/JETIR2506786.pdf