The BitMind Subnet is the world's first decentralized AI-generated content detection network. Built on Bittensor, our incentive mechanism rewards the most accurate detection algorithms, creating an adaptive defense against synthetic media.
The explosive growth of generative AI technology has unleashed an unprecedented wave of synthetic media creation. These AI-generated images, videos, and other content have become remarkably sophisticated, making them virtually indistinguishable from authentic media. This development presents a critical challenge to information integrity and societal trust in the digital age, as the line between real and synthetic content continues to blur.
To address this growing challenge, SN34 aims to create the most accurate fully-generalized detection system. Here, fully-generalized means that the system is capable of detecting both synthetic and semi-synthetic media with high degrees of accuracy regardless of their content or what model generated them. Our incentive mechanism evolves alongside state-of-the-art generative AI, rewarding miners whose detection algorithms best adapt to new forms of synthetic content.
This documentation assumes basic familiarity with Bittensor concepts. For an introduction, please check out the docs: https://docs.bittensor.com/learn/bittensor-building-blocks.
Miners
- Miners are tasked with running binary classifiers that discern between genuine and AI-generated content, and are rewarded based on their accuracy.
- Miners predict a float value in [0., 1.], with values greater than 0.5 indicating the image or video is AI generated.
Validators
- Validators challenge miners with a balanced mix of real and synthetic media drawn from a diverse pool of sources.
- We continually add new datasets and generative models to our validators in order to maximize coverage of the types of diverse data. Models and datasets are defined in
bitmind/validator/config.py.
Figure 1 (above): Ecosystem Overview
This diagram provides an overview of the validator neuron, miner neuron, and other components external to the subnet.
- The green arrows show how applications interact with the subnet to provide AI-generated image and video detection functionality.
- The blue arrows show how validators generate synthetic data, challenge miners and score their responses.
Figure 2 (above): Validator Components
This diagram presents the same architecture as figure 1, but with organic traffic ommitted and with a more detailed look at scored challenges and the associated validator neuron components.
Challenge Generation and Scoring (Blue Arrows)
For each challenge, the validator randomly samples a real or synthetic image/video from the cache, applies random augmentations to the sampled media, and distributes the augmented data to 50 randomly selected miners for classification. It then scores the miners responses, and logs comprehensive challenge results to Weights and Biases, including the generated media, original prompt, miner responses and rewards, and other challenge metadata.
Synthetic Data Generation (Pink Arrows):
The synthetic data generator coordinates a VLM and LLM to generate prompts for our suite of text-to-image, image-to-image, and text-to-video models. Each image or video is written to the cache along with the prompt, generation parameters, and other metadata.
Dataset Downloads (Green Arrows):
The real data fetcher performs partial dataset downloads, fetching random compressed chunks of datasets from HuggingFace and unpacking random portions of these chunks into the cache along with their metadata. Partial downloads avoid requiring TBs of space for large video datasets like OpenVid1M.
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