Page 716 - Emerging Trends and Innovations in Web-Based Applications and Technologies

International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
automatically extracted logos from various online platforms, brand assets and assist in legal actions against
including e-commerce websites and social media. Their tool counterfeiters. However, they also raised concerns about
was designed to filter out irrelevant images and focus on privacy issues related to web scraping and the potential for
logos, significantly speeding up the process of data collection. misuse of collected data.
However, they also pointed out that inconsistent website In summary, the intersection of AI-based image recognition
structures and CAPTCHA mechanisms posed significant and web scraping has made significant strides in addressing
challenges in scraping data from certain sources.
the issue of fake logos online. Previous studies have
In more recent years, the integration of AI models with web demonstrated the potential of deep learning models and web
scraping has gained momentum. A study by Li and Wong scraping tools in detecting counterfeit logos, but challenges
(2021) combined deep learning algorithms with web remain in terms of model accuracy, data quality, and ethical
scraping techniques to identify counterfeit logos across considerations. This research seeks to build upon these
multiple websites. Their approach utilized a combination of foundational studies by further exploring the combined
pre-trained CNNs and a custom-built scraper to gather logo effectiveness of AI models and web scraping techniques, with
images from various online sources. The results the goal of developing a more robust solution for fake logo
demonstrated that combining AI with web scraping could identification.
enhance the accuracy of logo identification while overcoming III. PROPOSED WORK
the limitations of standalone scraping or image classification This study proposes a comprehensive approach to
methods. However, the authors cautioned that there were
identifying fake logos on the internet by combining advanced
still issues related to data quality, as scraped images were
AI models and web scraping techniques. The first part of the
often noisy or contained multiple logos in a single frame.
proposed work involves the development and training of a
Another significant contribution to the field was made by Convolutional Neural Network (CNN) to effectively identify
Kim et al. (2020), who explored the use of Generative fake logos across various online platforms. The model will be
Adversarial Networks (GANs) to generate synthetic logos for trained using a large and diverse dataset of logos, including
training AI models. By creating realistic fake logos using both authentic and counterfeit examples, to ensure it can
GANs, their study aimed to augment the training dataset and generalize well to different types of fake logos. Techniques
improve the model’s ability to recognize counterfeits. While such as data augmentation and transfer learning will be
their approach showed promise in enhancing the diversity of employed to enhance the model’s robustness, enabling it to
the training data, the authors noted that it still faced handle variations in logo appearance, background noise, and
challenges in distinguishing between real and fake logos occlusion.
generated by sophisticated counterfeiters. This underscores
The second part of the proposed work focuses on optimizing
the importance of continuously updating training datasets
the web scraping process for efficient logo data collection. A
and refining AI models to stay ahead of evolving fraudulent
custom-built scraping framework will be designed to extract
techniques.
logo images from a variety of websites, with a focus on e-
Further research in this area has also focused on the ethical commerce platforms, social media, and other high-risk
and legal implications of detecting fake logos online. sources for counterfeit logos. The scraping tool will be
Intellectual property laws and digital rights management optimized to handle the challenges of inconsistent website
have become increasingly important as counterfeit goods structures and CAPTCHA mechanisms. The collected data
and fraudulent websites continue to proliferate. Studies by will then be used to continuously update and refine the AI
Sharma et al. (2022) emphasized the role of AI in enforcing model, ensuring that it stays effective in detecting new and
brand protection and reducing intellectual property evolving counterfeit logos. This integrated approach will aim
infringement. Their work highlighted the potential for AI- to provide a more scalable, accurate, and efficient solution
based logo detection systems to automate the monitoring of for identifying fake logos on the internet.

Fig. 1. The flow of proposed work
Data Collection
For this study, the primary source of data will be logo images collected from various online platforms, including e-commerce
websites, social media, and brand directories. The goal is to gather a diverse dataset of authentic logos and counterfeit logos

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