MIID (Multimodal Inorganic Identity Dataset) is a next-generation identity testing and identity data generation subnet designed to enhance fraud detection, KYC systems, and name-matching algorithms. Our goal is to provide financial institutions, security systems, and AI researchers with a robust dataset of name variations, transliterations, and identity attributes that help identify identity fraud and evasion techniques.
By incentivizing miners to create high-quality identity variations, MIID serves as a critical tool in financial crime prevention, identity resolution, and security intelligence.
Fraudsters use identity manipulation techniques to evade detection. Sanctioned individuals, high-risk entities, and money launderers exploit weaknesses in screening systems by using name variations, fake documents, and location obfuscation.
MIID tests and enhances these systems by:
- ✅ Simulating Identity-Related Risk Scenarios for AML and sanctions screening
- ✅ Evaluating Identity Matching Algorithms
- ✅ Providing Identity Data for Model Training
This network helps governments, financial institutions, and researchers improve their fraud detection models, making the financial ecosystem safer.
Miners process requests from validators and return identity data variations to enhance detection models.
- Receive mixed identity challenges from validators (KAV, UAV, and image-variation requests)
- Generate KAV variations: Name / DOB / Address
- Submit UAV location attack vectors that are unknown to LDS V1
- Generate face image variations from validator-provided seed images (Phase 4)
- Earn rewards based on accuracy, novelty, constraint adherence, and real-world adversarial value
Validators ensure the dataset maintains high-quality and real-world relevance.
- Issue challenge queries across KAV, UAV, and image variations
- Run online validation for immediate weight setting (where applicable)
- Perform post-validation to assess novelty/quality and update miner reputation for the next cycle
- Allocate rewards and continuously evolve the dataset for KYC/IDV resilience
- Python 3.10+
- Ollama (default LLM: llama3.1)
- Bittensor wallet with TAO
- 8GB+ RAM (16GB recommended)
- Open port 8091 for miner-to-validator communication (Network Setup Guide)
# Install dependencies
bash scripts/miner/setup.sh
# Activate the miner environment
source miner_env/bin/activate
# Start mining
pm2 start python --name neuron-miner -- neurons/miner.py --netuid 54 --wallet.name your-wallet --wallet.hotkey your-hotkey --subtensor.network finney
# Install dependencies
bash scripts/validator/setup.sh
# Activate the validator environment
source validator_env/bin/activate
# Start validating
pm2 start python --name neuron-validator -- neurons/validator.py --netuid 54 --wallet.name your_wallet --wallet.hotkey your_hotkey --subtensor.network finneyFor detailed instructions, check our Mining Guide and Validator Guide.
- Help banks, fintech, and law enforcement agencies strengthen fraud detection.
- Contribute to privacy-preserving AI research.
- Earn rewards while enhancing AI-driven name-matching and sanctions screening.
- Miners: Earn rewards for producing high-quality, diverse identity variations.
- Validators: Gain influence in network security and reward distribution.
MIID is not just another AI dataset—it's a live, evolving system that challenges and improves real-world fraud detection models. Every contribution makes financial systems safer and more secure.
Phase 1: Initial Launch & Name-Based Threat Scenarios (June 2025) Read more details here
- Deploy MIID subnet on Bittensor mainnet.
- Enable validators to test known threat scenarios against miner responses.
- Introduce name-based execution vectors: phonetic, orthographic, and rule-based variations.
- Expand Threat Scenario Query System to allow miners to propose unknown threat scenarios.
- Introduce a Post-Evaluation System to systematically validate and assess new miner-submitted threat scenarios.
- Support new evasion tactics, including nickname-based threats, transliteration-based alterations, and middle name manipulations.
- Improve validator scoring and introduce penalties for repetitive or low-value submissions.
- Add support for location-based unknown attack vectors (UAV) and obfuscation patterns.
- Establish post-validation workflows and LDS V1 (beta → full) to separate signal from noise.
- Use validated UAV quality to build a reputation signal that carries into future cycles.
- Introduce validator-provided seed face images and deepfake-style transformation families.
- Cycle 1 focuses on: pose_edit, lighting_edit, expression_edit, background_edit.
- Continue location UAV submissions unknown to LDS V1 while expanding adversarial identity testing.
- Phase 4 execution begins incorporating rewards based on validated UAV quality from Phase 3 Cycle 1.
- Expand biometric attack families beyond Cycle 1 (e.g., swap/recapture/morphing) (Q1 2026)
- Generate and validate synthetic documents (Q2 2026)
- Simulate digital presence and interactions (Q3 2026)
- Introduce financial transaction modeling (Q4 2026)
- Build 3D identity avatars (Q2 2027)
- Add voice and conversational AI support
- Train a comprehensive model for identity screening.
- Launch a decentralized platform for collaborative validation and contribution.
We are continuously improving MIID to:
- Expand identity data generation for enhanced AI benchmarking.
- Integrate more complex identity attributes (addresses, dates of birth, etc.).
- Improve fraud detection AI using multi-modal data sources.
Join us in shaping the future of identity verification and fraud prevention.
📢 Follow the project & contribute to our open-source development!
Discord | GitHub
This project is licensed under the MIT License - see the LICENSE file for details.
Built with ❤️ by the YANEZ-MIID Team