Online Fake Logo Detection

EasyChair Preprint 12665

Abstract

The proliferation of digital media and the ease
of content creation have given rise to a pressing issue – the
spread of fake logos. Protecting the integrity of brand identities
is crucial in the modern landscape, necessitating effective fake
logo detection mechanisms. This research endeavors to address
this challenge through the development of a robust detection
system using Python and web browser URLs.The method-
ology involves the acquisition of diverse datasets comprising
authentic and manipulated logos, laying the foundation for a
comprehensive training regimen. Employing convolutional neural
networks CNN and leveraging deep learning frameworks
like TensorFlow, the study aims to build a model capable of
discerning subtle variations indicative of counterfeit logos. Pre-
processing steps involve standardizing image sizes, normalizing
pixel values, and augmenting data for model generalization.
The model architecture incorporates convolutional layers for
feature extraction and dense layers for classification, fostering
the ability to distinguish between genuine and fabricated logos.To
facilitate real-world application, the system utilizes web scraping
techniques to extract logo images from web browser URLs. This
integration enables the model to assess logos encountered in
online environments, contributing to a proactive defense against
logo-based misinformation.The implementation involves loading
the trained model, pre processing web-scraped images, and
utilizing the model for predictions. The model’s performance
is evaluated based on its ability to accurately classify logos as
authentic or fake.

Keyphrases: 6. Deep learning, Flask, Image authenticity, Logo detection, Model Deployment, counterfeit detection, data preprocessing, feature extraction, file upload, image classification, image processing, machine learning, model evaluation, model training, web application

@booklet{EasyChair:12665,
  author    = {Venkata Vasu Sadineni and H Sainath Reddy and Akhil Kumar Vadde and Vijaya Bhaskar Vadde and M Krishna Raulji},
  title     = {Online Fake Logo Detection},
  howpublished = {EasyChair Preprint 12665},
  year      = {EasyChair, 2024}}