Abstract
TrustFlow – AI-Based Fake Review and Product Authenticity Detection Web Application is a web-based intelligent platform developed to assist users in identifying fake product reviews and verifying product authenticity through artificial intelligence. The system provides a centralized digital interface where users can easily paste product reviews or upload product descriptions along with images for authenticity analysis. The primary objective of the platform is to reduce the impact of misleading online information and improve trust in digital purchasing decisions through an accessible and user-friendly web solution. The application addresses common challenges faced by online consumers, including the presence of manipulated reviews, counterfeit product promotions, and lack of reliable verification mechanisms. By integrating modern web technologies and AI-based analysis models, the system enables real-time processing of textual and visual product data, accurate authenticity prediction, and clear result visualization. The structured login system, secure data handling, and intuitive navigation ensure that users can efficiently interact with the platform without requiring advanced technical knowledge. TrustFlow is developed using HTML, CSS, and JavaScript for frontend interface design, PHP for backend processing, and MySQL database management through phpMyAdmin within the XAMPP environment. Artificial intelligence processing is implemented using a Python backend integrated with Ollama and the LLaVA 7B multimodal model for review and product authenticity detection. These technologies ensure system scalability, reliability, and efficient performance during real-time usage. The platform also includes user activity tracking, authentication features, and modular system architecture to support future enhancements. Overall, TrustFlow contributes to improving digital consumer awareness by providing a transparent, intelligent, and efficient mechanism to detect fake reviews and suspicious products. The system aims to enhance online shopping confidence, support informed purchasing decisions, and promote the responsible use of AI-driven verification solutions in modern e-commerce environments.
Keywords
Fake Review Detection, Product Authenticity Verification, Artificial Intelligence, Web Application, PHP Backend, MySQL Database, XAMPP Server, Ollama AI Integration, LLaVA-7B Model, Consumer Trust Systems, Online Product Verification, Multimodal AI Analysis, Digital Commerce Security.
Citations
IRE Journals:
Prof. Sandhya Ranvir, Sakshi Shivaji Mahajan, Aadesh Harshad Sawant, Prathamesh Pradip Lokhande, Dhanraj Dipak Kale "Trust- Flow: Fake Product and Reviews Detection" Iconic Research And Engineering Journals Volume 9 Issue 9 2026 Page 1427-1431 https://doi.org/10.64388/IREV9I9-1715118
IEEE:
Prof. Sandhya Ranvir, Sakshi Shivaji Mahajan, Aadesh Harshad Sawant, Prathamesh Pradip Lokhande, Dhanraj Dipak Kale
"Trust- Flow: Fake Product and Reviews Detection" Iconic Research And Engineering Journals, 9(9) https://doi.org/10.64388/IREV9I9-1715118
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