Welcome to the BitMind Subnet! This repository contains all the necessary information to get started, contribute, and understand our architecture.
IMPORTANT: If you are new to Bittensor, we recommend familiarizing yourself with the basics on the Bittensor Website before proceeding to the Setup Guide page.
Overview: The BitMind Subnet leverages advanced generative and discriminative AI models within the Bittensor network to detect AI-generated images. This platform is engineered on a decentralized, incentive-driven framework to enhance trustworthiness and stimulate continuous technological advancement.
Purpose: The proliferation of generative AI models has significantly increased the production of high-quality synthetic media, presenting challenges in distinguishing these from authentic content. The BitMind Subnet addresses this challenge by providing robust detection mechanisms to maintain the integrity of digital media.
Features:
- API and Frontend (Coming Soon): Accessibility will be broadened through an intuitive API and user interface, facilitating the integration and utilization of our detection technologies.
- Model Evolution: Our platform continuously integrates the latest research and developments in AI to adapt to evolving generative techniques.
Core Components:
- Miners: Tasked with running binary classifiers that discern between genuine and AI-generated content.
- Foundation Model: Based on the insights from the 2024 CVPR paper Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection, our primary model utilizes Neighborhood Pixel Relationships to detect specific discrepancies in AI-generated images.
- Research Integration: We systematically update our detection models and methodologies in response to emerging academic research, offering resources like training codes and model weights to our community.
- Validators: Responsible for challenging miners with a balanced mix of real and synthetic images, drawn from a diverse pool of sources.
- Resource Expansion: We are committed to enhancing the validators' capabilities by increasing the diversity and volume of the image pool, which supports rigorous testing and validation processes.
Subnet Architecture Diagram
For real-time discussions, community support, and regular updates, join our Discord server. Connect with developers, researchers, and users to get the most out of BitMind Subnet.
This repository is licensed under the MIT License.
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