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On this page
  • What is ERC-7007?
  • Why is ERC-7007 Important?
  • How ERC-7007 Works
  • Core Components
  • Workflow Overview
  • Metadata Schema
  • Examples
  • Additional Resources

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  1. INITIAL MODEL OFFERING (IMO)

ERC-7007: Verifiable AI-Generated Content Token

Verifiable AI-Generated Content Token

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Last updated 7 months ago

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ERC-7007 is an extension of the ERC-721 non-fungible token (NFT) standard, specifically designed to accommodate verifiable AI-generated content on the Ethereum blockchain.

It can be paired with the in order to create inference assets that benefit model token holders.

What is ERC-7007?

ERC-7007 is a token standard that enables the creation and management of NFTs representing AI-generated content. It introduces a set of interfaces and events that allow for:

  • Verification of AI-Generated Content: Ensures that the content associated with an NFT is generated from a specific AI model using a given input (prompt).

  • Integration with zkML and opML: Supports Zero-Knowledge Machine Learning (zkML) and to verify the correctness of outputs.

  • Unique Token Identification: Assigns token IDs based on the prompt, ensuring that each NFT is uniquely associated with its input and output.

Why is ERC-7007 Important?

As AI-generated content becomes more prevalent, there is a need for a standardized way to represent and verify this content on the blockchain. ERC-7007 addresses several challenges:

  • Verification of Authenticity: Allows users to verify that the AI-generated content is produced by a specific model with a specific input.

  • Support for AI Model Authors and Content Creators: Provides a way for creators to monetize their AI models and content without risking open-source devaluation.

  • Enabling Revenue-Sharing Mechanisms: Facilitates fair compensation for AI model authors and content creators through secure and verifiable NFTs.

How ERC-7007 Works

Core Components

  1. AI Model: A pre-trained machine learning model that generates output based on a given input (prompt).

  2. zkML / opML:

    • zkML (Zero-Knowledge Machine Learning): Uses zero-knowledge proofs to verify that the content was generated correctly without revealing the model's details.

    • opML (Optimistic Machine Learning): Uses fraud-proofs where correctness is ensured through a challenge period and and onchain dispute resolution.

  • AIGC-NFT smart contract: An onchain contract compliant with ERC-7007, with full ERC-721 functionalities

  • Verifier smart contract: implements a verify function. When given an inference task and its corresponding ZK proof or opML finalization, it returns the verification result as a boolean.

Workflow Overview

zkML Scenario

  1. Model Publication: The AI model and its zero-knowledge proof verifier are published on Ethereum.

  2. Inference Task: A user submits an input (prompt) and initiates an inference task.

  3. Proof Generation: Nodes maintaining the model generate the output and a zero-knowledge proof of the inference.

  4. Verification: The proof is verified on-chain using the verifier contract.

  5. Minting NFT: The user owns the NFT representing the AI-generated content, secured by the proof.

opML Scenario

  1. Model Publication: The AI model is published on Ethereum using the ORA network.

  2. Inference Task: A user submits an input (prompt) and initiates an inference task.

  3. Result Submission: Nodes perform the inference and submit the output.

  4. Challenge Period: Other nodes can challenge the result within a predefined period.

  5. Finalization: After the challenge period, if unchallenged or successfully defended, the user can verify ownership and update the AIGC data as needed.

Metadata Schema

ERC-7007 introduces a standardized JSON schema for NFT metadata, including:

  • name: Identifies the asset.

  • description: Describes the asset.

  • image: URI pointing to an image representing the asset.

  • prompt: The input used to generate the AI content.

  • aigc_type: Type of AI-generated content (image, video, audio, etc.).

  • aigc_data: URI pointing to the AI-generated content.

  • proof_type: Indicates whether it's a validity proof (zkML) or fraud proof (opML).

Examples

Use Case: AI Art NFTs

An artist uses an AI model to generate artwork based on user-specified prompts:

  1. Content Generation: Users submit prompts during the minting process to generate unique artworks.

  2. Verification: Each piece is verified to confirm it was generated by the specific AI model using the provided prompt.

  3. NFT Minting: The verified art is minted as an ERC-7007 NFT, ensuring authenticity and uniqueness. Proceeds from the mint flow back to the artist.

Use Case: AI-Generated Music

  1. Model Publication: The AI music model is tokenized using the IMO process.

  2. User Interaction: Fans submit prompts or themes to generate personalized music tracks.

  3. Verification and Minting: Tracks are verified and minted as NFTs using ERC-7007 and ORA’s AI Oracle.

  4. Ownership and Royalties: The musician can implement royalty mechanisms for each NFT sale. Additionally, the use of the IMO allows token holders to receive a corresponding portion of the mint fees.

Additional Resources

  • Related Concepts:

Model Deployment: The artist deploys the AI model on Ethereum using the .

A musician tokenizes an AI model using an that generates music tracks:

ERC-7007 Proposal:

🪙
Initial Model Offering (IMO)
Optimistic Machine Learning (opML)
ORA Network
Initial Model Offering (IMO)
Ethereum Improvement Proposal
Initial Model Offering (IMO)
ERC-7641: Intrinsic RevShare Token