Page 715 - Emerging Trends and Innovations in Web-Based Applications and Technologies

International Journal of Trend in Scientific Research and Development (IJTSRD)
Special Issue on Emerging Trends and Innovations in Web-Based Applications and Technologies
Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470

Identifying Fake Logos on the Internet:
A Study of AI Models and Web Scraping Efficiency

Prof. Anupam Chaube

Department of Science and Technology,
G H Raisoni College of Engineering and Management, Nagpur, Maharashtra, India

ABSTRACT gathering large datasets of logos from the internet. Web
In the digital age, the prevalence of fake logos on the scraping tools enable the automatic extraction of relevant
internet poses a significant challenge to businesses, images from multiple web pages, providing the data needed
consumers, and the overall integrity of online branding. to train AI models. However, scraping the web for logos
This study explores the effectiveness of artificial presents its own set of challenges, including issues related to
intelligence (AI) models combined with web scraping website structure, data inconsistency, and the vast volume of
techniques for identifying counterfeit logos across various content available online.
online platforms. The research investigates the application
This study aims to evaluate the combined effectiveness of AI
of deep learning algorithms, such as convolutional neural
models and web scraping methods in identifying fake logos.
networks (CNNs), to recognize authentic logos and
The research will focus on the strengths and limitations of
distinguish them from their forged counterparts.
these technologies and propose potential solutions to
Additionally, the paper examines the role of web scraping
improve both the accuracy of AI-based detection and the
tools in efficiently collecting large datasets of logos from the
efficiency of web scraping for data collection.
internet for training and evaluation. The study highlights
key challenges, including the variability of fake logos, By investigating this intersection of AI and web scraping, this
website structure complexities, and data quality issues, study seeks to contribute to the ongoing efforts in securing
while also proposing solutions to improve model accuracy online environments, protecting intellectual property, and
and scraping efficiency. The findings suggest that while AI ensuring consumer trust in the digital marketplace.
models show promise in identifying fake logos, further II. RELATED WORK
refinement in both model architecture and scraping The issue of identifying fake logos on the internet has
methods is needed to enhance real-world application and
attracted attention from various fields, including image
scalability. This research aims to contribute to the ongoing recognition, web scraping, and cybersecurity. A number of
efforts in developing more secure and reliable online studies have explored the application of artificial intelligence
environments, benefiting both brand protection and
(AI) in detecting counterfeit logos, with a focus on leveraging
consumer trust.
deep learning models for effective classification. Early
research in this area often used traditional image processing
KEYWORDS: fake logos, artificial intelligence, AI models, web
techniques, such as feature extraction and template
scraping, deep learning, convolutional neural networks, logo
matching. However, these methods struggled to account for
identification, online branding
the wide variety of fake logos, which often vary in terms of
color, size, and distortion. As a result, more advanced AI-
I. INTRODUCTION
based approaches, particularly Convolutional Neural
The internet has revolutionized the way businesses and
Networks (CNNs), began to gain traction due to their ability
consumers interact, with logos serving as a vital component
to learn intricate patterns in image data without the need for
of brand identity. However, with the increasing presence of
manual feature engineering.
counterfeit goods and deceptive practices online, fake logos
have become a growing concern. These counterfeit logos are One notable study in this field is by Zhang et al. (2019), who
often used to mislead consumers, harm brand reputation, employed a CNN-based model to identify fake logos on e-
and enable fraudulent activities. Identifying these forged commerce websites. Their model showed promising results
logos manually is both time-consuming and prone to errors, in classifying logos, with high accuracy rates in detecting
which has led to the need for automated solutions to address counterfeits when trained on a dataset of authentic logos.
this problem. Despite these successes, the authors noted that challenges
such as logo occlusion and background noise still affected the
Artificial intelligence (AI) has emerged as a powerful tool in performance of their model, particularly when logos were
various image recognition tasks, including logo
distorted or embedded in complex images. This highlights
identification. Deep learning models, especially
the need for further refinement of AI models to handle these
Convolutional Neural Networks (CNNs), have proven highly
edge cases.
effective in distinguishing subtle differences between
authentic and fake logos. This research aims to explore the In parallel, web scraping has become an essential tool for
efficiency of AI models in the detection of counterfeit logos collecting large-scale datasets of logos for training AI models.
across a variety of online sources, such as e-commerce Several studies have examined the role of web scraping in
platforms, social media, and unofficial websites. automating the collection of data from online sources, with a
focus on its efficiency and scalability. For instance, Gupta et
In parallel with AI, web scraping plays a crucial role in al. (2018) developed a web scraping framework that
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