Page 645 - 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
Detection System aims to address these gaps by leveraging a counterfeit logos:
combination of AI, web scraping, and advanced image  Feature Extraction:
recognition techniques to provide a scalable and efficient ·
solution for detecting counterfeit logos. The CNN extracts visual features like:
o Shapes and patterns.
III. PROPOSED WORK o Fonts and typography.
The proposed Fake Logo Detection System combines o Colors and gradients.
Artificial Intelligence (AI) and web scraping techniques to o Symmetry and alignment.
identify counterfeit logos efficiently. This system is designed
 Training:
to automate the detection process by analyzing the design ·
features of logos and comparing them with authentic ones The model is trained using a labeled dataset to classify
logos as authentic or counterfeit.
stored in a database. The entire workflow is divided into
multiple phases, as detailed below: · The training process involves minimizing loss functions
1. Data Collection via Web Scraping and improving accuracy through iterative learning.
Web scraping serves as the foundation for building a robust  Validation:
dataset. In this phase: · The trained model is validated using test data to ensure
 Sources: Logos are scraped from multiple online it generalizes well to unseen logos.
platforms, such as: 4. Logo Comparison and Matching
· E-commerce websites: To detect fake products using Once the model is trained, it can compare any submitted logo
counterfeit logos. with authentic logos in the database:
· Social media platforms: To identify fraudulent  Similarity Scoring:
accounts and ads. · The system computes a similarity score between the
· Digital marketplaces: To gather logos used in product submitted logo and genuine logos.
listings. · A threshold value determines whether a logo is
classified as authentic or fake.
 Tools and Techniques:
· Libraries like BeautifulSoup and Scrapy are used to  Detailed Analysis:
scrape images and metadata from websites. · Inconsistencies such as mismatched fonts, incorrect
· Automated bots extract images and accompanying color schemes, or improper alignments are highlighted.
information, such as product descriptions, brand names, · The analysis also pinpoints specific areas of the logo that
and URLs. deviate from the genuine design.
 Challenges Addressed: 5. System Output and Reporting
· Duplicate or low-quality images are filtered during the The system generates actionable outputs to help users and
scraping process. brands take corrective measures:
· Irrelevant images and non-logo elements are removed  Detection Results:
using image classification models or heuristics. · A detailed report is created, including:
2. Dataset Preparation · The classification result (authentic or counterfeit).
Once the data is collected, it is pre-processed to ensure the
model receives clean, high-quality input: · Highlighted inconsistencies in the logo design.
· Confidence scores and feature-based analysis.
 Categorization:
· Logos are categorized as authentic or counterfeit based  Alerts and Notifications:
on known sources or brand databases. · If counterfeit logos are detected on monitored platforms,

 Data Augmentation: the system sends alerts to brands or administrators.
· To enhance the robustness of the AI model, the dataset is · Reports can be exported for legal or business purposes.
augmented using techniques like:
o Rotation. 6. Scalability and Automation
o Scaling. To make the system scalable and efficient:
o Flipping.  Real-Time Monitoring:
o Color adjustments. · The system continuously scrapes online platforms for
 Metadata Association: new logos, updating the database and training data in
· Relevant metadata such as image resolution, source, and real time.
timestamp is attached to each logo for better  Self-Learning:
organization. · With every detection cycle, the system refines its model
 Labeling: by incorporating new data into the training process.
· Counterfeit logos are manually labeled during the initial
 Cloud Integration:
stages to create a supervised learning dataset. · The system can be deployed on cloud platforms to
3. AI-Based Detection Model handle large datasets and ensure fast processing speeds.
The core of the system is the AI model, specifically a
Convolutional Neural Network (CNN), designed to detect

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