Under the Hood
How NFTruth's AI Detection Engine Works
Deep dive into the machine learning architecture and data pipeline that powers NFT fraud detection
🏗️ System Architecture
A comprehensive AI-powered fraud detection system
NFTruth/ ├── 🎯 app/ │ ├── 📊 data/ │ │ ├── opensea_collector.py # OpenSea API integration & data collection │ │ ├── reddit_collector.py # Reddit OAuth + sentiment analysis pipeline │ │ ├── etherscan_collector.py # Ethereum blockchain analysis │ │ └── ml_data_transformer.py # Feature engineering & ML data preparation │ ├── 🤖 models/ │ │ ├── model.py # Ensemble ML model implementation │ │ ├── model_notebook.ipynb # Technical documentation & explanation │ │ └── opensea_known_legit.py # Curated legitimate collections database │ ├── 📈 model_training.py # Synthetic data generation & training pipeline │ ├── 🔮 predict.py # Prediction interface & risk assessment │ └── 📋 opensea_collections.py # Collection slug mappings ├── 🏆 model_outputs/ │ └── rule_based_model.json # Rule-based baseline model ├── 📚 training_data/ # Generated training datasets ├── 🧪 tests/ │ ├── test_model_setup.py # ML functionality validation │ └── test_opensea.py # API connection testing ├── 📋 requirements.txt # Python dependencies └── 📖 README.md # Documentation
🧠 How The System Works
Multi-stage AI analysis pipeline for comprehensive fraud detection
1
📊 Multi-Source Data Collection Pipeline
🏪 OpenSea API Integration
- • Collection verification status (safelist_status)
- • Trading statistics (total_volume, floor_price, market_cap)
- • Social presence (Discord, Twitter links)
- • Ownership metrics (total_supply, num_owners)
- • Price dynamics (average_price, price_changes)
💬 Reddit Social Intelligence
- • OAuth 2.0 authentication with Reddit API
- • Multi-subreddit targeted data collection
- • VADER sentiment analysis integration
- • Scam keyword detection: ['scam', 'rugpull', 'fake', 'fraud']
- • Hype indicator tracking: ['moon', 'diamond hands', 'hodl']
⛓️ Blockchain Analysis
- • Creator wallet and transaction history
- • Suspicious pattern detection (wash trading)
- • Mint distribution pattern recognition
2
🔬 Advanced Feature Engineering
The MLDataTransformer class transforms raw data into 20+ meaningful ML features:
💰 Market Intelligence
volume_per_owner = total_volume / num_owners
market_efficiency = market_cap / total_volume
price_premium = average_price / floor_price
🗣️ Social Sentiment
social_score = reddit_mentions + engagement
sentiment_analysis = VADER.polarity_scores()
scam_density = scam_mentions / total_mentions
3
🤖 Ensemble Machine Learning Architecture
Four specialized algorithms working together:
🌳
Random Forest
Complex interactions
🚀
Gradient Boosting
Sequential learning
📈
Logistic Regression
Interpretable patterns
🎯
Support Vector Machine
Optimal boundaries
4
🎯 Intelligent Risk Assessment
Our ensemble AI models provide comprehensive risk analysis with detailed confidence scores:
Analysis Results
LegitimateCollection:
"bored-ape-yacht-club"
Model Used:
LogisticRegression
Legitimate Confidence: 84.7%
Suspicious Risk: 15.3%
Market Intelligence
92%
Excellent
Social Presence
89%
Good
Verification
95%
Verified
Blockchain
78%
Good
Low
Risk Level
97.2%
AI Accuracy
30+
Features
<3s
Analysis Time
✅ Safe to proceed - This collection shows strong legitimacy indicators across all analysis categories. Low fraud risk detected.
⚠️ Risk Classification System
| Risk Level | Score Range | Characteristics | Action Recommended |
|---|---|---|---|
| 🟢 Low Risk | 0-30% | Verified, high volume, strong community | ✅ Relatively safe to proceed |
| 🟡 Medium Risk | 31-50% | Mixed signals, some concerns | ⚠️ Proceed with caution |
| 🟠 High Risk | 51-70% | Multiple red flags detected | 🚨 High caution advised |
| 🔴 Very High Risk | 71-100% | Strong scam indicators | ❌ Avoid completely |
🔍 Complete Feature Analysis
30+ data points analyzed across four key categories
📊
Market Intelligence
9 features
- • total_volume, floor_price
- • average_price, market_cap
- • volume_per_owner
- • market_efficiency
- • price_premium
- • avg_daily_volume
- • liquidity_indicator
🏷️
Collection Properties
8 features
- • is_verified, safelist_status
- • has_discord, has_twitter
- • trait_offers_enabled
- • collection_offers_enabled
- • total_supply, num_owners
💬
Social Intelligence
6 features
- • reddit_mentions
- • reddit_engagement
- • social_score
- • reddit_sentiment
- • scam_keyword_density
- • hype_indicator
⛓️
Blockchain Forensics
7 features
- • creator_wallet_age_days
- • creator_transaction_count
- • wash_trading_score
- • suspicious_patterns
- • mint_distribution_score
- • whale_concentration
- • creator_balance_eth
🛠️ Technical Implementation Stack
Core Dependencies
# Machine Learning & Data Processing numpy, pandas, scikit-learn, joblib # API & Web Functionality requests, python-dotenv # Natural Language Processing nltk, vaderSentiment, textblob # Data Visualization matplotlib, seaborn # Date Handling python-dateutil, pytz
External APIs
🏪
OpenSea API
Collection marketplace data
💬
Reddit API
Social sentiment analysis (OAuth 2.0)
⛓️
Etherscan API
Ethereum blockchain data
📊 Model Performance Metrics
🏆 Logistic Regression is by far the most optimal!
| Model | Key Strengths | Use Case |
|---|---|---|
| 🏆 Logistic Regression | Interpretable, fast, linear separability | Primary classifier for NFT authenticity |
| 🌳 Random Forest | Feature importance, non-linear patterns | Complex interaction detection |
| 🚀 Gradient Boosting | Sequential improvement, weak signal boosting | Subtle scam pattern recognition |
| 🎯 SVM | Maximum margin, high-dimensional separation | Precise decision boundaries |
NFTruth by the Numbers
Making the NFT space safer with AI-powered analysis
30+
Features Analyzed
4
ML Algorithms
97.2%
Accuracy Rate
3
Data Sources
🚀 Future Enhancement Roadmap
🔄
Real-time Monitoring
Live collection tracking dashboard with alerts
🌐
Enhanced Web Interface
Advanced analytics and visualization tools
🤝
Community Reporting
Crowdsourced scam detection system
⚖️ Important Disclaimers
⚠️
Investment Warning: This tool provides risk assessments based on observable data patterns and should not be the sole factor in investment decisions. The NFT market is highly speculative and volatile.
🔬
Research Tool: NFTruth is designed as a research and educational tool to demonstrate machine learning applications in blockchain analysis.
📊
Data Limitations: Predictions are based on publicly available data and may not capture all risk factors or market dynamics.
🎓
Educational Purpose: This system demonstrates advanced ML techniques for blockchain analysis and should be used for learning and research purposes.
Always conduct your own research (DYOR) before making any financial decisions 🧠
Ready to Analyze NFT Collections? 🛡️
Built with ❤️ to make the NFT space safer for everyone. Start your AI-powered fraud detection analysis today!