Following the recent announcements of Google’s signalling of generative AI content and Midjourney and Shutterstock the day after, Microsoft has now announced that it will also be signalling the provenance of content created by Microsoft’s generative AI tools such as Bing Image Creator.

Microsoft’s efforts go one step beyond those of Google and Midjourney, because they are adding the image metadata in a way that can be verified using digital certificates. This means that not only is the signal added to the image metadata, but verifiable information is added on who added the metadata and when.

As TechCrunch puts it, “Using cryptographic methods, the capabilities, scheduled to roll out in the coming months, will mark and sign AI-generated content with metadata about the origin of the image or video.”

The system uses the specification created by the Coalition for Content Provenance and Authenticity. a joint project of Project Origin and the Content Authenticity Initiative.

The 1.3 version of the C2PA Specification specifies how a C2PA Action can be used to signal provenance of Generative AI content. This uses the IPTC DigitalSourceType vocabulary – the same vocabulary used by the Google and Midjourney implementations.

This follows IPTC’s guidance on how to use the DigitalSourceType property, published earlier this month.

As a follow-up to yesterday’s news on Google using IPTC metadata to mark AI-generated content we are happy to announce that generative AI tools from Midjourney and Shutterstock will both be adopting the same guidelines.

According to a post on Google’s blog, Midjourney and Shutterstock will be using the same mechanism as Google – that is, using the IPTC “Digital Source Type” property to embed a marker that the content was created by a generative AI tool. Google will be detecting this metadata and using it to show a signal in search results that the content has been AI-generated.

A step towards implementing responsible practices for AI

We at IPTC are very excited to see this concrete implementation of our guidance on metadata for synthetic media.

We also see it as a real-world implementation of the guidelines on Responsible Practices for Synthetic Media from the Partnership on AI, and of the AI Ethical Guidelines for the Re-Use and Production of Visual Content from CEPIC, the alliance of European picture agencies. Both of these best practice guidelines emphasise the need for transparency in declaring content that was created using AI tools.

The phrase from the CEPIC transparency guidelines is “Inform users that the media or content is synthetic, through
labelling or cryptographic means, when the media created includes synthetic elements.”

The equivalent recommendation from the Partnership on AI guidelines is called indirect disclosure:

“Indirect disclosure is embedded and includes, but is not limited to, applying cryptographic provenance to synthetic outputs (such as the C2PA standard), applying traceable elements to training data and outputs, synthetic media file metadata, synthetic media pixel composition, and single-frame disclosure statements in videos”

Here is a simple, concrete way of implementing these disclosure / transparency guidelines using existing metadata standards.

Moving towards a provenance ecosystem

IPTC is also involved in efforts to embed transparency and provenance metadata in a way that can be protected using cryptography: C2PA, the Content Authenticity Initiative, and Project Origin.

C2PA provides a way of declaring the same “Digital Source Type” information in a more robust way, that can provide mechanisms to retrieve metadata even after the image was manipulated or after the metadata was stripped from the file.

However implementing C2PA technology is more complicated, and involves obtaining and managing digital certificates, among other things. Also C2PA technology has not been implemented by platforms or search engines on the display side.

In the short term, AI content creation systems can use this simple mechanism to add disclosure information to their content.

The IPTC is happy to help any other parties to implement these metadata signals: please contact IPTC via the Contact Us form.

The IPTC has updated its Photo Metadata User Guide to include some best practice guidelines for how to use embedded metadata to signal “synthetic media” content that was created by generative AI systems.

After our work in 2022 and the draft vocabulary to support synthetic media, the IPTC NewsCodes Working Group, Video Metadata Working Group and Photo Metadata Working Group worked together with several experts and organisations to come up with a definitive list of “digital source types” that includes various types of machine-generated content, or hybrid human and machine-generated media.

Since publishing the vocabulary, the work has been picked up by the Coalition for Content Provenance and Authenticity (C2PA) via the use of digitalSourceType in Actions and in the IPTC Photo and Video Metadata assertion. But the primary use case is for adding metadata to image and video files

Here is a direct link to the new section on Guidance for using Digital Source Type, including examples for how the various terms can be used to describe media created in different formats – audio, video, images and even text.

IPTC recommends that software creating images using trained AI algorithms uses the “Digital Source Type” value of “trainedAlgorithmicMedia” is added to the XMP data packet in generated image and video files. Alternatively, it may be included in a C2PA manifest as described in the IPTC assertion documentation in the C2PA specification.

The official URL for the full vocabulary is http://cv.iptc.org/newscodes/digitalsourcetype, so the complete URI for the recommended Trained Algorithmic Media term is http://cv.iptc.org/newscodes/digitalsourcetype/trainedAlgorithmicMedia.

Other terms in the vocabulary include:

• Composite with synthetic elements – https://cv.iptc.org/newscodes/digitalsourcetype/compositeSynthetic – covering a composite image that contains some synthetic and some elements captured with a camera;
• Digital Art – https://cv.iptc.org/newscodes/digitalsourcetype/digitalArt – covering art created by a human using digital tools such as a mouse or digital pencil, or computer-generated imagery (CGI) video
• Virtual recording – https://cv.iptc.org/newscodes/digitalsourcetype/virtualRecording – a recording of a virtual event which may or may not contain synthetic elements, such as a Fortnite game or a Zoom meeting
• and several other options – see the full list with examples in the IPTC Photo Metadata User Guide.

Of course, the original digital source type values covering photographs taken on a digital camera or phone (digitalCapture), scan from negative (negativeFilm),  and images digitised from print (print) are also valid and may continue to be used. We have, however, retired the generic term “softwareImage” which is now deemed to be too generic. We recommend using one of the newer terms in its place.

If you are considering implementing this guidance in AI image generation software, we would love to hear about it so we can offer advice and tell others. Please contact us using the IPTC contact form.