System Overview
Understanding the dual-system architecture of v9.3
CopyrightChains v9.3 (CC93) Architecture
v9.3 consists of TWO COMPLEMENTARY SYSTEMS working together to provide comprehensive music verification and fraud detection.
System 1: AI Detection
Identifies AI-generated music from 8+ platforms
System 2: Playlist Intelligence
Detects streaming fraud and bot activity
System 1: AI Music Generation Detection
Detects whether tracks were created by AI platforms (Suno, Udio, AIVA, etc.) or human artists.
Supported AI Platforms
Suno
Risk: 85% copyright, 70% legal, 90% ownership
Udio
Risk: 80% copyright, 65% legal, 85% ownership
AIVA
Risk: 40% copyright, 30% legal, 45% ownership
Soundraw
Risk: 35% copyright, 25% legal, 40% ownership
Soundful
Risk: 35% copyright, 25% legal, 40% ownership
Mubert
Risk: 45% copyright, 35% legal, 50% ownership
Beatoven
Risk: 30% copyright, 25% legal, 35% ownership
Endel
Risk: 25% copyright, 20% legal, 30% ownership
Technology Stack
- Multi-AI Analysis: OpenAI GPT-4, Anthropic Claude Opus 4.1, Perplexity Sonar
- Audio Processing: librosa (Python audio analysis library)
- Metadata APIs: Spotify Web API, YouTube Data API v3, Deezer API
- Real-time Communication: WebSocket (port 8766)
- Database: MariaDB via MCP toolbox
Analysis Process
- Track Selection: User selects track from database
- Metadata Enrichment: Fetch data from Spotify, YouTube, Deezer
- Audio Download: Retrieve 30-second preview (if available)
- Feature Extraction: Analyze spectral, temporal, and timbral features
- Multi-AI Analysis: Query 3 AI platforms for platform identification
- Risk Assessment: Calculate confidence-scaled risk scores
- Results Display: Show platform likelihood and risk breakdown
System 2: Playlist Intelligence & Fraud Detection
Detects streaming fraud, bot activity, and fake engagement in Spotify playlists.
Technology Stack
- Machine Learning: XGBoost (fraud detection), Ensemble (artist authenticity)
- Statistical Analysis: Benford's Law, time-series anomalies, clustering
- Behavioral Analytics: Bot pattern detection, coordinated activity
- Network Analysis: Graph-based fraud network mapping
- Engagement Metrics: 50+ metrics across 7 dimensions
Fraud Detection Dimensions
1. Stream Velocity
Sudden spikes vs organic growth patterns
2. Engagement Quality
Skip rates, completion rates, listener diversity
3. Curator Authenticity
5-tier classification system
4. Network Analysis
Coordinated bot networks and farms
5. Behavioral Patterns
Bot listening signatures vs human variance
6. Artist Authenticity
Cross-platform verification and history
7. Financial Impact
Estimated revenue from fraudulent streams
Playlist Fraud Score (0-100)
- 0-20: Clean (organic engagement)
- 21-40: Low risk (minor irregularities)
- 41-60: Moderate risk (investigation recommended)
- 61-80: High risk (likely fraud)
- 81-100: Critical risk (definite fraud)
Unified Track + Playlist Verification
When both systems are complete, you can analyze tracks and playlists together for comprehensive verification.
Combined Workflow
- Track Selection: Choose track from database
- AI Detection: System 1 analyzes generation likelihood
- Playlist Analysis: System 2 analyzes playlists containing track
- Network Mapping: Identify coordinated fraud involving AI tracks
- Combined Risk Score: Aggregate risk assessment
- Evidence Package: Generate court-admissible report
Use Case 1
Independent artist verifies authenticity before signing with label
Use Case 2
Rights holder detects AI content theft and files dispute
Use Case 3
Playlist curator ensures legitimate engagement metrics
Use Case 4
Legal team compiles evidence for copyright infringement case
Technical Architecture
Services
- enhanced_ai_detector.py: Main verification service
- audio_analyzer.py: librosa feature extraction
- api_enrichment.py: Spotify/YouTube/Deezer integration
- playlist_processor.py: Fraud detection engine (planned)
- fraud_detector.py: ML model inference (planned)
Infrastructure
- MCP Server: Port 8091 (WebSocket), 5-second polling
- Processor Service: Port 8766 (WebSocket), Python 3.11
- Web UI: Port 80 (HTTP), Vanilla JavaScript
- Database: MariaDB external connection
Docker Containers
- cc93-mcp-server: Node.js + MCP toolbox
- cc93-processor: Python 3.11 + librosa
- cc93-webui: Nginx static server
v9.2 vs v9.3 Comparison
v9.2 (Production) Focus:
- Blockchain-based copyright registration
- Musical works and recordings management
- 16/84 royalty distribution
- Wyoming Series LLC creation
- Investigation and dispute resolution
v9.3 (Beta) Focus:
- AI music generation detection
- Playlist intelligence and fraud detection
- Real-time verification
- Machine learning fraud detection
- Network analysis for coordinated fraud
Integration
v9.3 complements v9.2 by adding:
- Pre-registration verification: Detect AI before registering
- Ongoing monitoring: Detect fraud in registered works
- Enhanced dispute evidence: AI generation proof
- Rights protection: Identify infringement of AI-generated content