ERC-7007 AI Content Verification Tokens

ERC-7007 is a new Ethereum token standard that aims to provide a way to verify the authenticity and origin of AI-generated content. This standard introduces a novel concept of “AI Content Verification Tokens” that can be used to track and certify AI-generated content, ensuring transparency and accountability in the rapidly growing AI content creation space.

The introduction of ERC-7007 addresses the growing concerns around the potential misuse of AI-generated content, such as deepfakes, misinformation, and plagiarism. By providing a verifiable record of the AI model used, the input data, and the output content, this standard enables content consumers to make informed decisions about the trustworthiness and reliability of the AI-generated content they encounter.

Introduction to ERC-7007

Hey there, folks! Let’s dive into the world of ERC-7007, a game-changing standard that’s shaking up the realm of AI-generated content. Imagine a world where you can verify the authenticity of AI-created art, music, or any other digital asset with just a few clicks. That’s precisely what ERC-7007 aims to achieve!

graph TD
    A[ERC-7007 Standard] --> B[Verifiable AI-Generated Content]
    B --> C[Enhanced Trust and Transparency]
    C --> D[Secure Ecosystem for AI Creations]

At its core, ERC-7007 is a standard that allows for the creation of verifiable AI-generated content tokens. It’s like a digital stamp of approval that says, “Hey, this content was indeed created by an AI, and here’s the proof!” Pretty neat, right?

But why do we need such a standard, you might ask? Well, with the rapid advancements in AI technology, we’re seeing an explosion of AI-generated content flooding the digital landscape. While this is undoubtedly exciting, it also raises concerns about authenticity, ownership, and intellectual property rights. ERC-7007 aims to address these challenges by providing a secure and transparent framework for verifying and tracking AI-generated content.

Explanation of the diagram:

The diagram illustrates the core concept of the ERC-7007 standard, which is to enable the creation of verifiable AI-generated content tokens.
It starts with the ERC-7007 standard itself, which leads to the ability to create verifiable AI-generated content.
This verifiability enhances trust and transparency in the ecosystem, ultimately leading to a secure environment for AI creations to thrive.

So, whether you’re an artist, a musician, a game developer, or simply a lover of all things AI-generated, ERC-7007 promises to revolutionize the way we interact with and appreciate these digital marvels. Get ready to embark on a journey where authenticity takes center stage, and the line between human and AI creativity blurs in the most fascinating way!

Motivation Behind ERC-7007

You know, the world of AI-generated content is really fascinating, but it also comes with its own set of challenges. One of the biggest issues we’ve been facing is the lack of proper tools to verify the authenticity and provenance of AI-generated content. Sure, we’ve been using ERC-721 tokens for a while now, but they just don’t cut it when it comes to AI-generated stuff.

Think about it this way: when you create a traditional digital artwork or collectible, it’s pretty straightforward to prove that you’re the original creator. But with AI-generated content, things get a bit more complicated. How can you be sure that the AI model used to generate that content was legit? What if someone tries to pass off AI-generated content as their own original work? That’s where the need for verifiable AI-generated content tokens comes into play.

flowchart LR
    A[Traditional Digital Art] --> B[ERC-721 Tokens]
    C[AI-Generated Content] --> D[Need for Verification]
    D --> E[ERC-7007]

This diagram illustrates the motivations behind the development of ERC-7007. While traditional digital art can be represented using ERC-721 tokens, AI-generated content requires additional verification mechanisms to ensure authenticity and provenance, leading to the need for a new standard like ERC-7007.

You see, with ERC-7007, we’re not just dealing with digital assets; we’re dealing with the entire process of AI-generated content creation. We need to be able to verify the AI models used, the prompts or inputs provided, and the resulting outputs. That’s why ERC-7007 incorporates advanced technologies like Zero-Knowledge Machine Learning (zkML) and Optimistic Machine Learning (opML) to ensure the integrity of AI-generated content.

But it’s not just about verification; it’s also about creating a fair and transparent ecosystem for AI model authors and content creators. By having a standardized way to represent and verify AI-generated content, we can enable new revenue-sharing models, secure sharing of AI models, and even fractional ownership of AI-generated art.

So, while ERC-721 tokens have served us well in the past, the world of AI-generated content demands a more robust and specialized solution. That’s where ERC-7007 comes in, promising to revolutionize the way we think about AI-generated content and its place in the world of blockchain and decentralized applications.

Key Features of ERC-7007

The ERC-7007 standard introduces several key features to address the challenges surrounding AI-generated content and its verification. Let’s dive into these features and explore how they work.

Integration of Zero-Knowledge Machine Learning (zkML)

One of the core components of ERC-7007 is the integration of Zero-Knowledge Machine Learning (zkML). This technology allows for the verification of AI-generated content without revealing the underlying model or training data. It’s like having a secret sauce that can confirm the authenticity of the content without giving away the recipe.

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# Example of using zkML for content verification
from zkml import ZKVerifier

verifier = ZKVerifier(model_hash)
is_valid = verifier.verify(content, proof)

In this example, we create a ZKVerifier instance with the hash of the AI model used to generate the content. The verify method takes the content itself and a cryptographic proof, and returns a boolean indicating whether the content is valid (i.e., generated by the specified model).

sequenceDiagram
    participant User
    participant Contract
    participant zkML
    User->>Contract: addAigcData(content, proof)
    Contract->>zkML: verify(content, proof)
    zkML-->>Contract: isValid
    Contract-->>User: AigcData event

This sequence diagram illustrates the process of adding AI-generated content data to the ERC-7007 contract. The user provides the content and a zkML proof, which is then verified by the zkML component. If the verification is successful, the contract emits an AigcData event, indicating that the content has been added and verified.

Implementation of Optimistic Machine Learning (opML)

In addition to zkML, ERC-7007 also incorporates Optimistic Machine Learning (opML). This mechanism allows for the verification of AI-generated content through a challenge-response process. If someone disputes the validity of the content, they can initiate a challenge, and the content creator must provide proof of its authenticity.

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// Example of using opML for content verification
contract ERC7007 {
    mapping(uint256 => AigcData) public aigcData;
    mapping(uint256 => uint256) public challengePeriods;

    function addAigcData(uint256 tokenId, bytes proof) public {
        aigcData[tokenId] = AigcData(msg.sender, proof);
        challengePeriods[tokenId] = block.number + 100; // 100 block challenge period
    }

    function challenge(uint256 tokenId) public {
        require(block.number < challengePeriods[tokenId], "Challenge period expired");
        // Initiate challenge process
    }
}

In this Solidity example, the addAigcData function allows users to add AI-generated content data (represented by a proof) for a specific token ID. A challenge period is set, during which other users can initiate a challenge by calling the challenge function. If a challenge is initiated, the content creator must provide additional proof or risk having the content invalidated.

Functions: `addAigcData` and `verify`

ERC-7007 introduces two new functions: addAigcData and verify. The addAigcData function allows users to associate AI-generated content data with a specific token ID. This data can include the prompt used to generate the content, the AI model used, and any relevant metadata.

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function addAigcData(uint256 tokenId, bytes calldata aigcData) external;

The verify function, on the other hand, is used to validate the AI-generated content data associated with a token ID. This function can be called by anyone to ensure the authenticity and integrity of the content.

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function verify(uint256 tokenId) external view returns (bool);

New Event: `AigcData`

ERC-7007 introduces a new event called AigcData. This event is emitted whenever AI-generated content data is added or updated for a specific token ID. The event provides transparency and allows interested parties to track changes to the AI-generated content data.

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event AigcData(uint256 indexed tokenId, bytes aigcData);

Optional Extensions: Enumerable and Updatable

ERC-7007 also includes optional extensions for enumerability and updatability. The Enumerable extension allows for efficient enumeration of all token IDs with associated AI-generated content data. This can be useful for applications that need to display or process all AI-generated content.

The Updatable extension, on the other hand, allows for the updating of AI-generated content data associated with a token ID. This can be useful in scenarios where the content needs to be modified or updated based on new information or improvements to the AI model.

Metadata JSON Schema for AIGC-NFTs

To standardize the metadata associated with AI-generated content NFTs (AIGC-NFTs), ERC-7007 defines a JSON schema. This schema specifies the required and optional fields that should be included in the metadata, such as the prompt used to generate the content, the AI model used, and any relevant metadata about the content itself.

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{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "Metadata JSON Schema for AIGC-NFTs",
  "type": "object",
  "properties": {
    "prompt": {
      "type": "string",
      "description": "The prompt used to generate the AI content"
    },
    "model": {
      "type": "string",
      "description": "The AI model used to generate the content"
    },
    // ... other properties specific to the AI-generated content
  },
  "required": ["prompt", "model"]
}

This JSON schema ensures consistency and interoperability across different AIGC-NFT platforms and applications, making it easier to share and consume AI-generated content metadata.

classDiagram
    class ERC7007Contract {
        -mapping(uint256 => AigcData) aigcData
        -mapping(uint256 => uint256) challengePeriods
        +addAigcData(uint256, bytes) external
        +verify(uint256) external view returns (bool)
        +challenge(uint256) external
    }
    
    class AigcData {
        +address creator
        +bytes proof
    }
    
    ERC7007Contract *-- AigcData

This class diagram illustrates the core components of the ERC-7007 contract. The ERC7007Contract class maintains mappings for AI-generated content data (aigcData) and challenge periods (challengePeriods). It provides functions for adding AI-generated content data (addAigcData), verifying the data (verify), and initiating challenges (challenge). The AigcData class represents the AI-generated content data, including the creator’s address and a proof.

By incorporating these key features, ERC-7007 aims to provide a robust and transparent framework for verifying and managing AI-generated content on the blockchain. This standard paves the way for a wide range of applications and use cases involving AI-generated content, as we’ll explore in the next section.

How ERC-7007 Works

The ERC-7007 standard introduces a novel approach to handling AI-generated content (AIGC) on the Ethereum blockchain. It addresses the limitations of existing ERC-721 tokens and provides a robust framework for verifying the authenticity and provenance of AI-generated assets. Let’s dive into the intricate workings of this groundbreaking standard.

Adding AI-Generated Content Data

The process of adding AIGC data to the blockchain is facilitated by the addAigcData function. This function allows content creators to submit their AI-generated content along with relevant metadata, such as the prompt used to generate the content, the AI model employed, and any other pertinent information.

Here’s an example of how the addAigcData function might be implemented in Solidity:

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function addAigcData(
    string memory prompt,
    string memory modelName,
    bytes memory aigcData,
    bytes memory zkml,
    bytes memory opml
) public returns (uint256 tokenId) {
    tokenId = _tokenIdCounter.current();
    _tokenIdCounter.increment();

    _prompts[tokenId] = prompt;
    _modelNames[tokenId] = modelName;
    _aigcData[tokenId] = aigcData;
    _zkml[tokenId] = zkml;
    _opml[tokenId] = opml;

    emit AigcData(tokenId, msg.sender, prompt, modelName, aigcData, zkml, opml);
}

In this example, the addAigcData function takes the prompt, model name, AIGC data, Zero-Knowledge Machine Learning (zkML) proof, and Optimistic Machine Learning (opML) proof as input parameters. It then stores this information in various mappings, indexed by the tokenId, and emits an AigcData event with the relevant details.

Verification Mechanisms: zkML and opML

ERC-7007 employs two distinct verification mechanisms to ensure the authenticity and integrity of AI-generated content: Zero-Knowledge Machine Learning (zkML) and Optimistic Machine Learning (opML).

Zero-Knowledge Machine Learning (zkML)

Zero-Knowledge Machine Learning (zkML) is a cryptographic technique that allows for the verification of AI-generated content without revealing the underlying AI model or training data. It leverages zero-knowledge proofs to prove that the content was indeed generated by a specific AI model, without disclosing the model itself.

In the context of ERC-7007, content creators can provide a zkML proof along with their AIGC data. This proof can be verified on-chain to confirm the authenticity of the content without exposing the AI model or training data.

Optimistic Machine Learning (opML)

Optimistic Machine Learning (opML) is a complementary verification mechanism that allows for the detection of potential issues or discrepancies in AI-generated content. It operates on the principle of “optimistic verification,” where content is initially accepted as valid, but can be challenged by other parties if they suspect any irregularities.

In the ERC-7007 standard, content creators can submit an opML proof along with their AIGC data. This proof serves as a commitment to the integrity of the content and can be used to initiate a challenge period during which other parties can scrutinize the content for potential issues.

If a challenge is raised and successfully verified, the content can be marked as invalid or removed from the system.

Role of Prompts in Indexing tokenId

In the context of AI-generated content, prompts play a crucial role in determining the nature and characteristics of the output. ERC-7007 recognizes this importance and leverages prompts as a means of indexing and organizing tokenIds.

When adding AIGC data using the addAigcData function, the prompt used to generate the content is stored in the _prompts mapping, indexed by the tokenId. This allows for efficient retrieval and organization of AI-generated content based on the prompts used.

For example, if multiple content creators generate artwork using the same prompt, their tokenIds will be associated with that prompt, enabling easy discovery and comparison of the different interpretations by various AI models.

sequenceDiagram
    participant User
    participant Contract

    User->>Contract: addAigcData(prompt, modelName, aigcData, zkml, opml)
    Note right of Contract: Store prompt, modelName, aigcData, zkml, opml
with tokenId as key
    Contract-->>User: tokenId

    User->>Contract: getAigcData(tokenId)
    Contract-->>User: prompt, modelName, aigcData, zkml, opml

    User->>Contract: getAigcDataByPrompt(prompt)
    Note right of Contract: Retrieve all tokenIds
associated with the prompt
    Contract-->>User: List of tokenIds

This sequence diagram illustrates the process of adding AIGC data, retrieving data by tokenId, and retrieving a list of tokenIds associated with a specific prompt.

By indexing tokenIds based on prompts, ERC-7007 facilitates efficient organization, discovery, and exploration of AI-generated content, enabling users to easily find and compare different interpretations of the same prompt by various AI models.

Mermaid Diagram Explanations

Sequence Diagram

The sequence diagram illustrates the interactions between a user and the ERC-7007 contract. It showcases the following key operations:

addAigcData: The user calls this function to add AIGC data, including the prompt, model name, AIGC data itself, and the zkML and opML proofs. The contract stores this information mapped to the assigned tokenId and returns the tokenId to the user.
getAigcData: The user can retrieve the AIGC data associated with a specific tokenId by calling this function. The contract returns the prompt, model name, AIGC data, and the zkML and opML proofs.
getAigcDataByPrompt: The user can retrieve a list of tokenIds associated with a specific prompt by calling this function. The contract searches through the _prompts mapping and returns all tokenIds that match the provided prompt.

This diagram visually represents the core functionality of adding, retrieving, and organizing AIGC data based on tokenIds and prompts within the ERC-7007 contract.

Overall, the ERC-7007 standard introduces a comprehensive framework for handling AI-generated content on the Ethereum blockchain. By leveraging zkML and opML verification mechanisms, as well as indexing tokenIds based on prompts, it enables secure and transparent management of AIGC assets, fostering trust and accountability in the rapidly evolving field of AI-generated content.

Real-World Use Cases of ERC-7007

The ERC-7007 standard opens up a world of possibilities for verifiable AI-generated content tokens. It’s like having a digital stamp of authenticity for the creations that come from the minds (or circuits) of our artificial friends. Let me walk you through some exciting use cases that could revolutionize the way we think about AI-generated content.

AI-Generated Art NFTs

Imagine being able to own a piece of art that was crafted by an AI, but with the added assurance that it’s the real deal. ERC-7007 tokens could be used to represent AI-generated artworks as NFTs, complete with verifiable metadata about the AI model, prompts, and parameters used to create the artwork. This opens up a whole new market for AI-generated art, where collectors can confidently invest in these digital masterpieces.

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# Example Python code for minting an AI-generated art NFT
from aigc_nft import AigcNft

# Initialize the AI model and generate the artwork
ai_model = load_model('stable-diffusion')
artwork = ai_model.generate('A surreal landscape with floating islands and a giant oak tree')

# Mint the NFT with the AI-generated content data
nft = AigcNft.mint(
    artwork=artwork,
    ai_model_name='Stable Diffusion',
    ai_model_version='1.4',
    prompt='A surreal landscape with floating islands and a giant oak tree',
    seed=42,
    guidance_scale=7.5
)

print(f"AI-generated art NFT minted with token ID: {nft.token_id}")

AI-Generated Music NFTs

Music is another artistic realm where AI is making waves. With ERC-7007, you could tokenize AI-generated music compositions, complete with metadata about the AI model, prompts, and parameters used to create the music. This could open up new avenues for musicians and composers to explore AI-assisted creativity while maintaining verifiable ownership and provenance of their works.

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// Example Solidity contract for minting AI-generated music NFTs
pragma solidity ^0.8.0;

import "@openzeppelin/contracts/token/ERC721/ERC721.sol";
import "./ERC7007.sol";

contract AiMusicNft is ERC721, ERC7007 {
    constructor(string memory name, string memory symbol) ERC721(name, symbol) {}

    function mintAiMusic(
        string memory prompt,
        string memory aiModelName,
        string memory aiModelVersion,
        bytes32 seed,
        uint256 temperature
    ) public returns (uint256) {
        uint256 tokenId = totalSupply() + 1;
        _safeMint(msg.sender, tokenId);

        addAigcData(
            tokenId,
            prompt,
            aiModelName,
            aiModelVersion,
            abi.encodePacked(seed, temperature)
        );

        return tokenId;
    }
}

Dynamic In-Game Assets

Gaming is another industry that could benefit greatly from ERC-7007. Imagine a game where the environment, characters, or assets are dynamically generated by an AI based on player input or game state. With ERC-7007 tokens, these AI-generated assets could be tokenized and verifiably owned by players, adding a new layer of ownership and value to the gaming experience.

sequenceDiagram
    participant Player
    participant Game
    participant AiAssetGenerator
    participant ERC7007Contract

    Player->>Game: Start game
    Game->>AiAssetGenerator: Generate assets based on game state
    AiAssetGenerator->>ERC7007Contract: Mint AI-generated asset NFT
    ERC7007Contract-->>AiAssetGenerator: NFT token ID
    AiAssetGenerator-->>Game: AI-generated assets with token IDs
    Game-->>Player: Display AI-generated assets in game

Fractional Ownership of AI Art

ERC-7007 could also enable fractional ownership of AI-generated artworks. By tokenizing an AI-generated artwork as an ERC-7007 token, ownership could be divided into multiple shares, allowing multiple individuals to co-own the artwork. This could democratize access to valuable AI-generated art and create new investment opportunities.

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// Example Solidity contract for fractional ownership of AI art
pragma solidity ^0.8.0;

import "@openzeppelin/contracts/token/ERC721/extensions/ERC721Enumerable.sol";
import "./ERC7007.sol";

contract FractionalAiArt is ERC721Enumerable, ERC7007 {
    constructor(string memory name, string memory symbol) ERC721(name, symbol) {}

    mapping(uint256 => uint256) public totalShares;
    mapping(uint256 => mapping(uint256 => uint256)) public sharesPerOwner;

    function mintFractionalAiArt(
        string memory prompt,
        string memory aiModelName,
        string memory aiModelVersion,
        bytes32 seed,
        uint256 numShares
    ) public returns (uint256) {
        uint256 tokenId = totalSupply() + 1;
        _safeMint(msg.sender, tokenId);
        totalShares[tokenId] = numShares;
        sharesPerOwner[tokenId][msg.sender] = numShares;

        addAigcData(
            tokenId,
            prompt,
            aiModelName,
            aiModelVersion,
            abi.encodePacked(seed)
        );

        return tokenId;
    }

    // ... (transfer and share management functions)
}

Decentralized AI Training Data Management

ERC-7007 could also play a role in the management of AI training data. By tokenizing training data as ERC-7007 tokens, you could create a decentralized marketplace for AI training data, where data owners can sell or license their data to AI model developers. The verifiable metadata in ERC-7007 tokens could ensure the provenance and quality of the training data, fostering trust and transparency in the AI development process.

graph TD
    A[Data Owners] -->|Tokenize data| B(ERC7007 Tokens)
    B --> C{Decentralized Marketplace}
    D[AI Model Developers] -->|Purchase tokens| C
    C -->|Transfer tokens| D
    D -->|Train models| E[AI Models]

These are just a few examples of the exciting possibilities that ERC-7007 brings to the table. As AI continues to advance and permeate more aspects of our lives, having a standard for verifiable AI-generated content tokens could be a game-changer, ensuring transparency, ownership, and trust in the AI-powered creations of the future. The ERC-7007 standard introduces several key benefits that address the challenges faced in the realm of AI-generated content. Let’s explore these benefits in detail:

Enhanced Verification of AI-Generated Content

One of the primary advantages of ERC-7007 is its ability to verify the authenticity and provenance of AI-generated content. By integrating Zero-Knowledge Machine Learning (zkML) and Optimistic Machine Learning (opML) techniques, the standard ensures that the content tokenized on the blockchain can be reliably traced back to the AI model and the specific prompts used during generation.

sequenceDiagram
    participant User
    participant AIModel
    participant ERC7007Contract
    participant Verifier

    User->>AIModel: Provide prompts
    AIModel-->>User: Generate AI content
    User->>ERC7007Contract: Call addAigcData()
    ERC7007Contract->>Verifier: Initiate verification
    Verifier-->>ERC7007Contract: Verification result
    ERC7007Contract-->>User: Verified AI content token

As illustrated in the sequence diagram above, the addAigcData function triggers the verification process, which involves cross-checking the AI-generated content against the claimed AI model and prompts. This verification step is crucial for establishing trust and credibility in the AI-generated content ecosystem.

Support for AI Model Authors and Content Creators

ERC-7007 provides a framework that supports both AI model authors and content creators. By tokenizing AI-generated content, the standard enables creators to monetize their work and potentially implement revenue-sharing mechanisms with the AI model authors.

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# Example of a revenue-sharing mechanism
def share_revenue(tokenId, recipient, amount):
    token_data = erc7007_contract.getAigcData(tokenId)
    ai_model_address = token_data.ai_model_address
    
    # Split revenue between content creator and AI model author
    creator_share = amount * 0.7
    model_author_share = amount * 0.3
    
    # Transfer shares
    erc7007_contract.transferFrom(msg.sender, recipient, creator_share)
    erc7007_contract.transferFrom(msg.sender, ai_model_address, model_author_share)

This Python code snippet demonstrates a hypothetical revenue-sharing mechanism, where a portion of the revenue generated from an AI-generated content token is distributed to the AI model author’s address. Such mechanisms can incentivize the development and sharing of high-quality AI models, fostering innovation in the field.

Potential for Revenue-Sharing Mechanisms

Building upon the previous point, ERC-7007 paves the way for the implementation of revenue-sharing mechanisms between content creators and AI model authors. By tokenizing AI-generated content and associating it with the respective AI model, the standard enables transparent and automated distribution of royalties or revenue shares.

Secure Sharing of AI Models Without Financial Devaluation

One of the significant challenges in the AI industry is the potential devaluation of AI models due to unauthorized sharing or replication. ERC-7007 addresses this issue by providing a secure and verifiable way to share AI models without compromising their financial value. By tokenizing the AI-generated content and associating it with the original AI model, the standard ensures that the model’s intellectual property rights are respected, and its value is preserved.

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// Example of secure AI model sharing using ERC-7007
contract AIModelLicenseToken is ERC7007 {
    mapping(uint256 => address) public tokenIdToAIModelOwner;

    function mintAIModelLicense(address aiModelOwner, uint256 tokenId) public {
        require(tokenIdToAIModelOwner[tokenId] == address(0), "Token ID already in use");
        tokenIdToAIModelOwner[tokenId] = aiModelOwner;
        _safeMint(msg.sender, tokenId);
    }

    function useAIModel(uint256 tokenId) public view returns (address) {
        require(tokenIdToAIModelOwner[tokenId] != address(0), "Invalid token ID");
        return tokenIdToAIModelOwner[tokenId];
    }
}

In this Solidity code example, an AIModelLicenseToken contract inherits from the ERC-7007 standard and implements a mechanism for securely sharing AI models. Content creators can mint licenses (ERC-7007 tokens) associated with specific AI models, and these licenses can be traded or shared without compromising the AI model’s intellectual property rights or financial value.

By addressing these key benefits, ERC-7007 aims to foster a thriving ecosystem for AI-generated content, where creators, AI model authors, and users can interact with confidence, transparency, and fair compensation.

Implementation Considerations

When it comes to implementing the ERC-7007 standard for verifiable AI-generated content tokens, there are several important considerations to keep in mind. These considerations revolve around security aspects, handling data changes during challenge periods, and ensuring backward compatibility with existing standards.

Security Aspects and Frontrunning Risks

One of the key security concerns with ERC-7007 is the potential for frontrunning attacks. Frontrunning occurs when a malicious actor exploits the transparency of blockchain transactions to gain an unfair advantage. In the context of ERC-7007, a frontrunner could potentially monitor the mempool for pending transactions and attempt to submit a conflicting transaction with a higher gas price, effectively “cutting in line” and potentially invalidating the original transaction.

To mitigate this risk, ERC-7007 implementations should consider incorporating measures such as commit-reveal schemes or other cryptographic techniques to obscure the content of transactions until they are mined and included in a block.

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// Example of a commit-reveal scheme in Solidity
contract CommitReveal {
    mapping(bytes32 => bytes32) public commits;
    mapping(bytes32 => bool) public reveals;

    function commit(bytes32 _commit) public {
        require(commits[_commit] == 0, "Already committed");
        commits[_commit] = keccak256(abi.encodePacked(msg.sender));
    }

    function reveal(bytes32 _commit, bytes32 _value) public {
        bytes32 senderCommit = commits[_commit];
        require(senderCommit != 0, "No commit found");
        require(!reveals[_commit], "Already revealed");
        require(senderCommit == keccak256(abi.encodePacked(msg.sender)), "Invalid commit");

        reveals[_commit] = true;
        // Do something with the revealed value
    }
}

In this example, users first commit to a hash of their value (commit function), and then later reveal the actual value (reveal function). This prevents frontrunners from seeing the value until it’s too late to front-run the transaction.

Handling AIGC Data Changes During Challenge Periods

Another consideration is how to handle changes to the AI-generated content (AIGC) data during the challenge period, when the data is being verified using Zero-Knowledge Machine Learning (zkML) or Optimistic Machine Learning (opML) techniques.

sequenceDiagram
    participant User
    participant Contract
    participant Challenger
    participant Verifier

    User->>Contract: addAigcData(tokenId, data)
    Contract->>Contract: Start challenge period
    loop Challenge Period
        Challenger-->>Contract: Challenge data
        Contract-->>Verifier: Initiate verification
        Verifier-->>Contract: Verification result
    end
    Contract-->>User: Data accepted or rejected

One approach is to allow updates to the AIGC data during the challenge period, but with the caveat that each update restarts the challenge period. This ensures that any changes to the data are subject to the same verification process and gives challengers sufficient time to scrutinize the updated data.

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# Example in Python
def handle_aigc_data_update(token_id, new_data):
    # Reset the challenge period for the given token ID
    challenge_periods[token_id] = (new_data, time.time() + CHALLENGE_PERIOD_DURATION)

    # Notify challengers of the updated data
    notify_challengers(token_id, new_data)

Another approach is to disallow updates to the AIGC data once the challenge period has started, ensuring that the verification process is focused on a fixed set of data. This approach may be simpler to implement but could be seen as overly restrictive in certain use cases.

Backward Compatibility with Existing Standards

Finally, it’s important to consider backward compatibility with existing standards, such as ERC-721 for non-fungible tokens. While ERC-7007 introduces new features and functionality specific to AI-generated content, it should still be compatible with existing infrastructure and tooling built around ERC-721.

One way to achieve this is by inheriting from the ERC-721 contract and extending its functionality with the additional features of ERC-7007. This approach allows ERC-7007 tokens to be used in the same way as traditional ERC-721 tokens, while also providing the added benefits of verifiable AI-generated content.

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// Example of an ERC-7007 contract inheriting from ERC-721
import "@openzeppelin/contracts/token/ERC721/ERC721.sol";

contract ERC7007 is ERC721 {
    // ... (ERC-7007 specific functionality)

    function addAigcData(uint256 tokenId, bytes memory data) public {
        // ... (implementation for adding AIGC data)
    }

    function verify(uint256 tokenId) public {
        // ... (implementation for verifying AIGC data)
    }
}

By carefully considering these implementation considerations, developers can ensure that their ERC-7007 implementations are secure, efficient, and compatible with existing standards, paving the way for widespread adoption and integration of verifiable AI-generated content tokens.

classDiagram
    class ERC7007 {
        <>
        -mapping(uint256 => AigcData) aigcDataMap
        -mapping(uint256 => ChallengeData) challengeDataMap
        +addAigcData(uint256 tokenId, bytes data)
        +verify(uint256 tokenId)
        +challengeData(uint256 tokenId, bytes proof)
    }

    class AigcData {
        <>
        +bytes data
        +uint256 timestamp
    }

    class ChallengeData {
        <>
        +bytes proof
        +uint256 challengeStartTime
        +uint256 challengeEndTime
    }

    ERC7007 *-- AigcData
    ERC7007 *-- ChallengeData

This class diagram illustrates the core components of an ERC-7007 contract implementation. The ERC7007 contract maintains mappings for AIGC data (aigcDataMap) and challenge data (challengeDataMap). The addAigcData function allows users to add AIGC data for a specific token ID, while the verify function initiates the verification process using zkML or opML techniques. The challengeData function allows challengers to submit proofs during the challenge period.

The AigcData struct stores the actual AIGC data and the timestamp when it was added, while the ChallengeData struct holds the proof submitted by a challenger, as well as the start and end times of the challenge period.

This diagram provides a high-level overview of the core components and relationships within an ERC-7007 implementation, helping developers understand the overall architecture and design considerations. ERC-7007 has the potential to revolutionize the way we perceive and interact with AI-generated content. By introducing a verifiable and transparent framework for tokenizing AI-generated creations, this standard opens up a world of possibilities for artists, creators, and innovators alike.

One of the most significant impacts of ERC-7007 is the enhanced trust and credibility it brings to AI-generated content. With the integration of cutting-edge technologies like Zero-Knowledge Machine Learning (zkML) and Optimistic Machine Learning (opML), content creators can now provide irrefutable proof of the authenticity and provenance of their AI-generated works. This not only protects the intellectual property rights of creators but also fosters a more transparent and accountable ecosystem for AI-generated content.

Moreover, ERC-7007 paves the way for new revenue streams and business models. By enabling the fractional ownership of AI-generated art and the secure sharing of AI models without financial devaluation, this standard opens up exciting opportunities for collaborative creation, decentralized AI training data management, and innovative monetization strategies.

Looking ahead, the future prospects of ERC-7007 are boundless. As AI technology continues to evolve and its applications expand, this standard could serve as a foundation for a wide range of use cases, from dynamic in-game assets to personalized AI-generated experiences. Additionally, the potential for integrating ERC-7007 with other emerging technologies, such as decentralized identity solutions and privacy-preserving computation, could further enhance the security, transparency, and functionality of AI-generated content tokenization.

To illustrate the potential impact and future prospects of ERC-7007, let’s consider a few visual representations using Mermaid diagrams:

graph TD
    A[ERC-7007] --> B[Verifiable AI-Generated Content]
    B --> C[Enhanced Trust and Credibility]
    B --> D[New Revenue Streams]
    B --> E[Collaborative Creation]
    B --> F[Decentralized AI Training Data]
    F --> G[Improved AI Model Performance]
    C --> H[Intellectual Property Protection]
    D --> I[Fractional Ownership]
    D --> J[Innovative Monetization]
    E --> K[Cross-Domain Collaboration]
    E --> L[Open Source AI Models]

This flowchart diagram illustrates the far-reaching implications of ERC-7007, ranging from enhanced trust and credibility in AI-generated content to new revenue streams, collaborative creation, and decentralized AI training data management. It also highlights the potential for improved AI model performance, intellectual property protection, fractional ownership, innovative monetization strategies, cross-domain collaboration, and open-source AI models.

sequenceDiagram
    participant Creator
    participant ERC-7007
    participant Verifier
    participant Consumer
    Creator->>ERC-7007: Mint AI-Generated Content Token
    ERC-7007->>Verifier: Request Verification
    Verifier->>ERC-7007: Provide Verification Proof
    ERC-7007->>Consumer: Display Verified AI-Generated Content
    Consumer->>Creator: Engage with Content (Purchase, Collaborate, etc.)

This sequence diagram illustrates the typical flow of verifying and consuming AI-generated content using ERC-7007. It showcases the interactions between the creator, the ERC-7007 standard, the verifier, and the consumer, highlighting the verification process and the potential for engagement and collaboration between creators and consumers.

As the adoption of ERC-7007 grows, we can expect to see a surge in innovative applications and use cases that leverage the power of AI-generated content while ensuring transparency, accountability, and fair compensation for creators and contributors. The future of AI-generated content is bright, and ERC-7007 is poised to play a pivotal role in shaping this exciting and rapidly evolving landscape.

Introducing ERC-7007: Verifiable AI-Generated Content Tokens

ERC-7007 introduces a new standard for verifiable AI-generated content tokens, enhancing authenticity and ownership in the digital art world.

ERC-7007 AI Content Verification Tokens

Introduction to ERC-7007

Motivation Behind ERC-7007

Key Features of ERC-7007

Integration of Zero-Knowledge Machine Learning (zkML)

Implementation of Optimistic Machine Learning (opML)

Functions: `addAigcData` and `verify`

New Event: `AigcData`

Optional Extensions: Enumerable and Updatable

Metadata JSON Schema for AIGC-NFTs

How ERC-7007 Works

Adding AI-Generated Content Data

Verification Mechanisms: zkML and opML

Zero-Knowledge Machine Learning (zkML)

Optimistic Machine Learning (opML)

Role of Prompts in Indexing tokenId

Mermaid Diagram Explanations

Sequence Diagram

Real-World Use Cases of ERC-7007

AI-Generated Art NFTs

AI-Generated Music NFTs

Dynamic In-Game Assets

Fractional Ownership of AI Art

Decentralized AI Training Data Management

Implementation Considerations

Security Aspects and Frontrunning Risks

Handling AIGC Data Changes During Challenge Periods

Backward Compatibility with Existing Standards

ERC-7007 AI Content Verification Tokens

Introduction to ERC-7007

Motivation Behind ERC-7007

Key Features of ERC-7007

Integration of Zero-Knowledge Machine Learning (zkML)

Implementation of Optimistic Machine Learning (opML)

Functions: addAigcData and verify

New Event: AigcData

Optional Extensions: Enumerable and Updatable

Metadata JSON Schema for AIGC-NFTs

How ERC-7007 Works

Adding AI-Generated Content Data

Verification Mechanisms: zkML and opML

Zero-Knowledge Machine Learning (zkML)

Optimistic Machine Learning (opML)

Role of Prompts in Indexing tokenId

Mermaid Diagram Explanations

Sequence Diagram

Real-World Use Cases of ERC-7007

AI-Generated Art NFTs

AI-Generated Music NFTs

Dynamic In-Game Assets

Fractional Ownership of AI Art

Decentralized AI Training Data Management

Implementation Considerations

Security Aspects and Frontrunning Risks

Handling AIGC Data Changes During Challenge Periods

Backward Compatibility with Existing Standards

Functions: `addAigcData` and `verify`

New Event: `AigcData`