This repository contains the code and resources for a fake logo detector model developed using computer vision techniques. The model aims to identify counterfeit or forged logos in images, helping businesses and organizations protect their brand integrity.

The Fake Brand Logo Detector is a deep learning-based tool that helps identify whether a brand logo is real or fake. The model classifies brand logos using a ResNet-18 architecture that has been fine-tuned for detecting logo authenticity. The project includes: A dataset of real and fake brand logos downloaded from Bing.

fake logo detection systems can help prevent the sale of counterfeit products and protect the reputation of brands. In this review, we aim to provide a comprehensive overview of the different techniques that have been used for detecting fake logos using Python. We will review the strengths and weaknesses of , describe the each technique[5]

We use Python and Machine learning for finding fake logo where we train our model using CNN Algorthim .

Fake logo identification has become a significant area of research due to the widespread use of logos in a range of digital media and the potential consequences of their misuse. This study proposes an innovative approach to detect bogus logos using Python.

Fake logo identification has become a significant area of research due to the widespread use of logos in a range of digital media and the potential consequences of their misuse. This study proposes an innovative approach to detect bogus logos using Python.

We describe a machine learning and Python-based logo identification and recognition application as a preventative measures against this. By analyzing logos to confirm their authenticity, this technology assists firms in preserving their reputation while assisting customers in …

This study proposes an innovative approach to detect bogus logos using Python. Because of the widespread use of logos in a range of digital media and the potential consequences of misusing them, fake logo detection has become a crucial area of research.

In [1]: Proposes an online fake logo detection system using machine learning to distinguish real and fake logos based on extracted features. In [2]: Uses image processing and deep learning techniques, particularly the YOLO algorithm, to detect fake logos and prevent online fraud.

Counterfeit logos can harm businesses and consumers. LogoGuard employs deep learning techniques to automatically identify counterfeit logos in images. This repository contains code and resources for building, training, and using the LogoGuard model.