Architecture Summary
Overview
BitMind operates as a distributed AI content detection platform with multiple interconnected components working together to provide accurate, scalable, and privacy-compliant deepfake detection services.
Architecture
🎯 BitMind Architecture Platform
Distributed AI Infrastructure on Bittensor Network
Architecture Flow 🔄
The BitMind platform operates through five interconnected layers:
🌐 User Layer: Multi-platform interfaces (web, mobile, browser extension, enterprise clients) provide seamless access
⚡ API Gateway: Routes requests through specialized endpoints for platform, subnet, and enterprise operations
☁️ Infrastructure: Leverages Modal for ML inference, Supabase for data management, and Cloudflare for global distribution
🧠 Bittensor Network: Powers decentralized AI with Subnet 34 for fraud detection, distributed miners, and validator nodes
💾 Storage Layer: Manages models and datasets across R2, Modal Volumes, and HuggingFace repositories
Core Components
1. AI Infrastructure (Bittensor Subnet 34)
Purpose: Decentralized AI (DeAI) network that realizes a continuous, dynamic, incentivized competition for producing accurate detection models and novel, high-signal training data.
Architecture:
Detection Miners: Submit packaged models (no live endpoints) for validator-side evaluation on curated benchmarks
Generation Miners: Adversarially generate high-signal synthetic media that evolves the benchmark
Validators: Evaluate submitted models locally, track benchmark versions, and distribute rewards based on measured performance
Adversarial Loop: Generators push realism; discriminators improve detection — both co-evolve under aligned incentives
Model Management: Automated submission, testing, and deployment flows
Key Features:
Fully-generalized detection (works across different generative AI models)
Continuous evolution with new generative AI
Economic incentives for high-quality models
Centralized evaluation inside validators improves privacy, determinism, and reproducibility
Open, renewable training data via shared public corpus
2. Infrastructure Services
Developer dashboard and miner performance stats
80% cost reduction vs monolithic design
Multi-version image processing
API key management and usage analytics
Smart GPU cache with auto-recovery
Video preprocessing and segmentation
Account administration and billing
Batch processing for efficiency
C2PA integration for content provenance
Integration with Bittensor ecosystem
Security validation for ONNX models
High-throughput processing
3. API Services
Purpose: Enterprise-grade detection interfaces for batch and realtime workloads.
Architecture:
Platform API: Synchronous image/video detection endpoints with version-pinned models
Batch API: Asynchronous bulk processing with job status and callbacks/webhooks
Versioning: Deterministic outputs via benchmark/model version pinning
Security: API key auth, rate limiting, and usage analytics
Key Features:
Zero data retention
High-volume batch processing
Deterministic, version-pinned evaluation outputs
Enterprise compliance (GDPR/SOC2-ready)
24/7 support
4. Consumer Applications
Purpose: End-user experiences for fast, trustworthy deepfake detection.
Architecture:
Mobile (iOS/Android): Native apps for camera/gallery input; supports offline cached models
Web (thedetector.ai): Uploads, URL-based analysis, direct image address; integrates with extension
Browser Extension: On-page scanning and one-click verification across major sites
Key Features:
Instant detection UX (mobile/web)
Offline capability for cached models (mobile)
URL/direct-address analysis (web)
Privacy-first design (extension)
Push notifications and usage insights (mobile)
Architectural Highlights
Detection Capabilities
Multi-Modal: Images and videos
Real-Time: Sub-second inference times
Batch Processing: High-volume enterprise support
Accuracy: State-of-the-art detection performance
Adversarial Benchmarking: Continuous refresh from generator submissions to surface new edge cases
Privacy & Compliance
Zero Data Retention: No input data stored
GDPR Compliant: Regional data residency
SOC2 Ready: Enterprise compliance standards
Audit Trail: Complete usage logging
Reproducible Evaluation: Results pinned to benchmark versions and model hashes
Scalability
Horizontal Scaling: Auto-scaling infrastructure
Global CDN: Low-latency worldwide access
Load Balancing: High availability
Fault Tolerance: Redundant systems
Performance Metrics
Detection Performance
Accuracy: >95% on benchmark datasets
Latency: <100ms for image inference
Throughput: 1000+ requests per second
Availability: 99.9% uptime guarantee
Infrastructure Performance
Global Latency: <200ms worldwide
Scalability: Auto-scaling to 10,000+ concurrent users
Reliability: 99.9% availability with redundancy
Security: Zero data breaches, SOC2 compliant
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