This is the seventh installment of our Tech article series. The goal of this multipart series is to discuss provenance at a high level and provide more technically inclined readers a full dive into how to execute digital provenance. You can find the full list of this series here :
Part 1: What is Provenance and why we need it
Part 2: What is C2PA, Add C2PA easily with Capture
Part 3: Take True Photo with C2PA Watermark from Mobile Phones
Part 4 : Add C2PA easily with Capture API
Part 5 : C2PA for Music and Video Platforms with Capture API
Part 6 : C2PA for Gen-AI company and dev with Capture API part 1
Part 7 : You are here
Overview
While C2PA (Coalition for Content Provenance and Authenticity) provides an open standard for embedding metadata in digital media and enabling verification of its origin, the challenge lies in being able to seamlessly incorporate these standards into generative AI platforms. Capture, by Numbers Protocol, offers a user-friendly toolkit that simplifies the process allowing for compliance without the need for extensive resources.
In our previous C2PA for Gen-AI article, we explored how to add C2PA watermarks into Generative AI workflows by leveraging Instill AI’s a no-code platform. In this article we will be expanding further and exploring how Numbers Protocol builds on top of C2PA standards to create robust verification.
One of the main challenges that Numbers Protocol provenance solution addresses is the ability to access and verify the provenance of an asset. By adhering to IPTC standards, Numbers Protocol ensures that all registered content is easily searchable, shareable and verifiable across different platforms and systems. This is achievable because of Numbers Protocol’s unique file indexing and use of blockchain immutability. In combination with C2PA standards, we have a very reliable and robust provenance solution.
Let’s see this in action:
Upload C2PA photo onto C2PA verify site. Acquire the Nid from the left hand column.
The goal of Numbers Protocol is to create traceable and verifiable digital media in an open and decentralized manner. It achieves this by indexing media files with a Numbers ID (Nid) and storing associated provenance records on the Numbers Blockchain. With this design, a file registered with Numbers Protocol can always be found and its full detailed records are immutable. Let us see how it works:
Numbers Protocol builds on top of C2PA to offer the most comprehensive and reliable provenance solution. The above verification flow takes us from the C2PA verification site, to acquiring Nid, to querying on Numbers Network, to viewing on-chain records via asset profile.
After taking note of the NID of the asset, let’s head over to the Numbers Verify engine.
Navigate to Numbers Verify and input in Nid into the search bar. Click Search.
Numbers Verify will display results. The Nid indexes the files registered to Numbers Protocol and ensures we can find associated provenance records. We can click to view its asset profile to view its comprehensive provenance records.
The asset profile contains a comprehensive summary of the registered media file. To view the on-chain records we can navigate to the commit table and click on the Metadata link.
The Metadata file of the content is recorded on the blockchain following ERC-7053 standard. By viewing the Metadata we can see the comprehensive provenance records of this asset. Important fields include digitalSourceType : “trainedAlgorithmicMedia” indicating it is an AI-generated media, generatedBy : “instill_model-stable_diffusion_xl” indicating the AI model used was instill stable diffusion model and generatedThrough: “https://instill.tech” telling us the software application used to create the media.
The amount of detail within the Metadata file can go even further. If we view the instillMetadata field we can see the exact construction of the AI pipeline. In the start operator we know that a prompt, license name, license document and creator was fed into the pipeline.
We can see that the instill model and numbers connector were used and what data was fed into each connector
We can also see that this AI pipeline outputs a final image and an asset profile link.
Why is Numbers Verify Engine required if there’s already a verification site from C2PA?
While embedding C2PA metadata into digital media is a great way to make assets verifiable it is not a full proof solution as it can be easily removed accidentally or intentionally. Most media platforms compress images and as a result strip the media of its embedded provenance. Additionally, screenshot images will also be without C2PA metadata. The fragility of metadata injections signals a need for a more robust verification solution.
This is where Numbers Protocol and its Verify engine comes into play to create traceable and verifiable digital media in an open and decentralized manner. It achieves this by indexing media files with a Numbers ID (Nid) and storing associated provenance records on the Numbers Blockchain. With this design, a file that is registered with Numbers Protocol can always be found and its records are immutable. Media can be verified and traced even if the file in question was stripped of its C2PA by social media or screenshotted.
Insights on AI Regulations and Compliance
One of the latest developments on AI aside from the tech is the EU AI Act, which aims to set comprehensive guidelines for the development and deployment of AI systems. Understanding and navigating these regulations is crucial for AI platforms to ensure compliance and build trust with users and stakeholders.
The EU AI Act is designed to establish a framework that balances innovation with safety and ethical considerations. For AI developers and product managers, this means staying informed about the requirements that apply to their technologies. The Act categorizes AI systems based on their potential risk, with generative AI falling into categories that demand higher levels of scrutiny and regulation. This includes mandatory requirements for transparency, such as labeling AI-generated content with an AI watermark and ensuring the traceability of AI models and their outputs.
OpenAI’s recent advancements highlight the importance of responsible AI practices and in response to the EU AI Act has committed itself to utilize C2PA. By embedding metadata standards like C2PA, OpenAI sets a benchmark for transparency and accountability in the AI industry. This aligns with the goals of Capture by Numbers Protocol, which strives to enhance the reliability of digital content through immutable records.
Moreover, regulatory bodies worldwide are increasingly recognizing the potential for misuse and ethical concerns associated with AI-generated content. This recognition is leading to a wave of new laws and guidelines aimed at safeguarding digital ecosystems. Early adoption of standards like C2PA, will give an AI platform a competitive edge, ensuring that you are not only compliant with current regulations but also well-prepared for future legislative changes. By integrating C2PA and leveraging tools like Capture, you can ensure that your generated AI content is both verifiable and compliant, positioning your platform as a trusted player in the evolving world of AI.
For further consultation or to learn more about our solution, feel free to reach out to us.
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