Secure Online Seller Registration System

Abstract

The rapid growth of e-commerce has led to an increase in counterfeit products and fraudulent sellers, causing financial losses to buyers and reducing trust in online marketplaces. Traditional verification systems are inadequate in detecting fraud, necessitating the implementation of advanced AI-driven solutions. This paper proposes an AI-powered marketplace that integrates Aadhaar-based seller verification, sentiment analysis for fraud detection, and multi-factor authentication (MFA) to ensure a secure and trustworthy e-commerce platform. Our system employs AI, natural language processing (NLP). The proposed solution reduces fraud risks, improves transparency, and builds consumer confidence in online purchases

Introduction

Overview:
E-commerce has transformed global shopping but faces major challenges like fraudulent sellers, counterfeit products, and security risks, which reduce consumer trust. Traditional verification methods are inefficient and error-prone, making advanced, technology-driven solutions essential.

Proposed Solution:

The paper proposes an AI-powered secure seller registration system that combines:

• Aadhaar-based identity verification

• Video consent with liveness detection

• AI-powered sentiment analysis of buyer reviews

• Blockchain-based digital signatures

These technologies automate seller verification, prevent fraud, and ensure a secure and trustworthy online marketplace.

Key Features:

1. Seller Registration & Verification:

   • Collects personal and product information.

   • Verifies identity through Aadhaar OCR and real-time video consent.

   • Uses digital signatures for legal consent.

   • Product listings are analyzed using NLP and image recognition to detect counterfeit or misleading items.

2. Buyer Feedback & Fraud Detection:

   • Buyers provide textual, image, and video reviews.

   • AI verifies authenticity via sentiment analysis and image matching.

   • Video feedback undergoes liveness detection to prevent fake reviews.

3. Reputation System:

   • Sellers earn a “Trusted Seller” badge based on positive verified feedback.

   • Sellers involved in fraud are blacklisted and blocked from future listings.

Architecture & Implementation:

• Frontend: Built with HTML, CSS, JavaScript, and Django.

• Backend: Uses Python (Flask) for authentication, verification, and feedback processing.

• Database: MySQL stores seller/buyer data, product listings, and reports.

• Includes a complaint system for buyers to report fraud, which is processed automatically.

Benefits:

• Enhanced Security: Prevents identity fraud and counterfeit product listings.

• Automated Fraud Detection: Reduces manual oversight and speeds up response.

• Increased Trust & Transparency: Promotes a safer and more reliable online marketplace.

• Operational Efficiency: Streamlines seller onboarding and feedback validation.

Conclusion

The Secure Online Seller Registration System enhances e-commerce security by leveraging AI-driven verification, NLP-based analysis, and sentiment-driven fraud detection. It ensures only verified sellers can list products while enabling buyers to provide AI-analyzed feedback, reducing counterfeit sales and fraudulent transactions. With Aadhaar authentication, video consent, and digital signatures, the system builds seller trust, while automated review analysis and complaint handling safeguard buyers. This project not only minimizes fraud but also empowers consumers with accurate information and secure transactions, setting a new standard for online seller verification and consumer protection.

References

[1] Gupta, R., & Sharma, P. (2023). AI-Based Fraud Detection in E-Commerce: Techniques and Applications. International Journal of Computer Science and Security, 17(2), 45-58. [2] Kumar, S., & Patel, A. (2022). Enhancing Online Seller Verification through Aadhaar-Based Authentication. Journal of Digital Identity Management, 10(1), 33-47. [3] Wang, L., & Chen, Y. (2021). Sentiment Analysis for Consumer Reviews in E-Commerce Using NLP. IEEE Transactions on Artificial Intelligence, 4(3), 120-135. [4] Raj, M., &Iyer, N. (2023). Video-Based Identity Verification: A New Era of Secure Online Registrations. Journal of Cybersecurity Research, 11(4), 77-90. [5] Smith, J., & Brown, K. (2022). Blockchain for Secure Seller Verification in Digital Marketplaces. International Journal of Blockchain Applications, 9(2), 150-163. [6] Das, B., & Roy, S. (2023). Consumer Protection in Online Transactions: AI-Driven Fraud Prevention. E-Commerce Security Journal, 15(1), 60-74. [7] Choudhary, A., & Mehta, P. (2021). NLP and Machine Learning in Fraud Detection: A Review. Journal of Data Science and AI, 8(3), 92-108. [8] Lee, C., & Park, H. (2022). AI-Powered Feedback Analysis for E-Commerce Platforms. Journal of Business Intelligence, 6(2), 39-52. [9] Verma, K., & Singh, R. (2023). Automated Review Analysis for Identifying Fake Sellers. International Conference on AI & Digital Fraud Prevention, 200-215. [10] World Economic Forum. (2022). The Future of Secure Digital Transactions. Global Digital Trust Report, 18-30.

Copyright

Copyright © 2025 Janani S, Afrin Banu M, Jerina Francisca J, Mrs. Priyadarshini P. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.