Platform for verifying the authenticity of antique auctions using Multimodal AI
About • Key Features • Architecture • Tech Stack • Repositories • Team
Antique Analyzer is a project created for the "Projekt zespołowy" (Team Project) course by a team of Computer Science Master's students at the University of Information Technology and Management in Rzeszów (UITM).
The platform addresses the problem of counterfeit antiques in e-commerce. It combines Artificial Intelligence (multimodal analysis of images and text) with Community Verification (experts and users) to assess the authenticity of items listed on platforms like Allegro, OLX, and eBay.
- 🔍 AI-Powered Verification: Uses EfficientNet-B0 (Vision) and DistilBERT (NLP) to analyze auction photos and descriptions, detecting potential scams or replicas.
- 🕷️ Automated Scraping: Intelligent web scrapers (Selenium, Apify) automatically fetch auction data from URL links.
- 👥 Expert Crowdsourcing: A dedicated panel for domain experts (Numismatics, Furniture, etc.) to validate AI results and provide professional opinions.
- 🗳️ Community Voting: A democratic voting system where the community can agree or disagree with the verdict.
- 📊 Confidence Scoring: The system provides an authenticity probability score (Original vs. Scam/Replica).
The system operates on a microservices architecture:
- Core Backend (Ruby on Rails): Manages users, auth, database, auctions, and community interactions.
- AI Service (Python/FastAPI): Exposes endpoints for scraping and running the ML inference.
- Frontend (React/TypeScript): A modern, responsive web interface.
Here are the open-source components of our platform:
| Component | Description | Tech |
|---|---|---|
| Analysis Model | Multimodal ML model & FastAPI service. | Python, PyTorch |
| Backend | Core logic, database management, and user handling. | Ruby on Rails |
| Frontend | The user interface for analyzing auctions and voting. | React, TypeScript |
| Web Scraper | Tools for fetching training data and live auction info. | Python, Selenium, Apify |
We are a team of Master's students passionate about AI and Data Science.
 Kamil Kukiełka Leader & Data Scientist |
 Bartosz Kawa Backend Developer |
 Michał Żychowski Data Engineer |
 Kamil Michna Frontend Developer |
