An image generated by Bing Image Creator using the prompt “A cute robot sitting at a French-style cast iron table in a sunny garden, drawing a picture in a notebook”. This image contains the new IPTC Photo Metadata fields “AI Prompt Information”, “AI System Used” and “AI Prompt Writer Name”. Note that adding the new IPTC Photo Metadata properties has rendered the C2PA metadata invalid.
The IPTC Photo Metadata Working Group has released version 2025.1 of the IPTC Photo Metadata Standard, including properties that can be used for AI-generated content.
The new properties are:
AI System Used
Definition: The AI engine and/or the model name used to generate this image.
User Note: For example, ChatGPT DALL-E, Google Gemini, ChatGPT Suggest help text: Enter the name of the AI system and/or the model name used to generate this image.
AI System Version Used
Definition: The version of the AI system used to generate this image, if known.
Suggested help text: Enter the name or number of the version of the AI system used to generate this image.
AI Prompt Information
Definition: The information that was given to the generative AI service as “prompt(s)” in order to generate this image.
User Note: This may include negative [excludes] and positive [includes] statements in the prompt.
Suggested help text: Enter the information given to the generative AI service as “prompt(s)” in order to generate this image.
AI Prompt Writer Name
Definition: Name of the person who wrote the prompt used for generating this image.
User Note: This person should not be considered as the image creator.
Suggested help text: Enter the name of the person who wrote the prompt used for generating this image.
IPTC’s specification materials have been updated to accommodate the new properties:
The new properties are expected to be implemented in software tools soon. The popular open-source tool Exiftool already supports the new properties, since version 13.40 which was released on October 24th 2025.
Thanks to everyone who contributed to our request for comments on these new properties. We made several changes based on feedback from IPTC members and others, so your contributions were well appreciated.
Tim Bray speaking about C2PA at the IPTC Photo Metadata Conference 2025.
Software industry legend Tim Bray gave a resounding call to IPTC and to others working on media provenance and C2PA: his verdict was that while the specification and its implementation had issues, they were slowly being resolved and he lauded the project’s goal of, in Tim’s words, “making it harder for liars to lie and easier for truth tellers to be believed”.
The 2025 Photo Metadata Conference, held on September 18th, was a great success, with 280 registered attendees from hundreds of organisations around the world. Video recordings from the event are now available.
Speakers included:
David Riecks, lead of the IPTC Photo Metadata Working Group, describing some new IPTC Photo Metadata properties concerning Generative AI models and prompts that will be proposed for a vote at the next Standards Committee meeting to be held at the IPTC Autumn Meeting.
Ottar A. B. Anderson, Head of Photography at SEDAK, the GLAM imaging service of Møre og Romsdal County in Norway, spoke about Metadata for Image Quality in Galleries, Libraries, Archives and Museums (GLAM) and his work on the Digital Object Authenticity Working Group.
AI caption tagging for the Superbowl – Jerry Lai, Senior Director of Content, Imagn Images – Download Jerry’s slides
Paul Reinitz, previously with Getty Images and now a consultant on business and legal issues around copyright, spoke about recent developments in the area including updates in the US, EU and China
Brendan Quinn spoke again to give an update on the IPTC’s work with C2PA and the world of Media Provenance, including our work on the Verified News Publishers List
Tim Bray, creator of tech standards like XML and Atom and companies like OpenText
Marcos Armstrong of CBC / Radio Canada spoke about his work on mapping image publishing workflows at CBC.
Feedback from the event was almost universally positive:
“While I knew I wouldn’t understand all of the terms, I was so impressed the amount of topics that were touched upon. I had no problem following along. I loved the passion and the openess to different perspectives”
“Great topic choices- perfect level of beginner/more advanced content presentation.”
“It was a good critical look at the pluses and minuses of various decisions being made, ultimately pointing to developing public trust about authorship.”
“Informative, I really liked the expertise all the speaker brought to the virtual table”
“Learning about strategies to protect from and tools for blocking AI, as well as metadata fields to record AI use”
“Informative, good presentations and presenters. Very relevant to today – AI.”
“Focus on Content Credentials and AI. Range of speaker roles provided different perspectives on the topic area. Excellent organization, presentation quality and management of the zoom space.”
“Three things in particular stood out. Tim Bray’s talk was great as it brought everything to my world as a photographer and is pretty much what I’ve found. Brendan Quinn’s opt out information was definitely worth knowing and now I’m going to look at it. Finally, David Riecks talk about Adobe’s Metadata Panel gave me more insight into it and if it should be included in my workflow but his information for the proposed new properties for Generative AI was very good to hear.”
Thanks to everyone who attended and to our speakers David, Brendan, Paul, Ottar, Tim and Marcos.
Special thanks to David and the IPTC Photo Metadata Working Group for organising the event.
We look forward to seeing even more attendees next year!
To be sure of being notified about next year’s event, subscribe to the (very low volume) “Friends of IPTC Newsletter”.
An image generated by Imagine with Meta AI, using the prompt “A cute robot penguin painting a picture of itself using a canvas mounted on a a wooden easel, in the countryside.” The image contains IPTC DigitalSourceType metadata showing that it was generated by AI. This image would be a candidate for the new photo metadata properties being proposed here.
The IPTC PhotoMetadata Working Groupis proposingadraft set of properties for recording details of images created using generative AI systems. The group presents a draft of these fields for your comments and feedback. After comments are reviewed the group intends to add new properties to a new version of the IPTC Photo Metadata Standard which would be released later in 2025.
Use Cases
The proposals detailed here are intended to address these scenarios, among others:
How do you know which system/model generated this image? For instance, if you wanted to compare how different systems—or versions of systems—interpret a given prompt, where would you look?
How can you know what prompt text was entered, or image shared as a starting point? If you want to recreate similar images in the future with the same look, where should that info be stored?
How can you tell who was involved in the creation of a generative AI image?
Example Scenario
You are the new designer for an organisation, and need to create an image for a monthly column. You are told to use generative AI, but your boss wants the end result to have the same “look and feel” as images used previously in the column. If you needed to find those that were published previously—what information would be most useful in locating and retrieving the image(s) in your organization’s image collection?
Proposed Properties:
AI Model
Name: AI Model
Definition: The foundational model name and version used to generate this image.
User Note: For example “DALL-E 2”, “Google Genesis 1.5 Pro”
Basic Specs: Data type: Text / Cardinality: 0..1
AI Text Prompt Description
Name: AI Text Prompt Description
Definition: The information that was given to the generative AI service as “prompt(s)” in order to generate this image.
User Note: This may include negative [excludes] and positive [includes] statements in the prompt.
Basic Specs: Data type: Text / Cardinality: 0..1
AI Prompt Writer Name
Name: AI Prompt Writer Name
Definition: Name of the person who wrote the prompt used for generating this image.
Basic Specs: Data type: Text / Cardinality: 0..1
Reference Image(s)
Name: Reference Image
Definition: Image(s) used as a starting point to be refined by the generative AI system (sometimes referred to as “base image”).
Basic Specs: Data type: URI / Cardinality: 0..unbounded
All of these properties are of course optional, not required, but we would recommend that AI engines fill in the properties whenever possible.
Request for Comment
The intent is for a new standard version including these fields to be proposed at the IPTC Autumn Meeting 2025 in October to be voted on by IPTC member organisations. If approved by members, the new version would be published in November 2025.
At the 2024 IPTC Photo Metadata Conference, James Lockman of Adobe’s Digital Media Services division demonstrated the Custom Metadata Panel, a tool that allows users to create their own user interface for editing sets of metadata fields. Since that time, the IPTC has worked with James and his team to make the tool even more useful, supporting the full set of IPTC properties and even enabling IPTC Photo Metadata as the default view in the tool.
Adobe’s Custom Metadata Panel offers the full set of IPTC Photo Metadata properties to users of Adobe Bridge, Photoshop, Premiere Pro and Illustrator.
The Custom Metadata Panel also supports IPTC’s equivalent standard for video content, IPTC Video Metadata Hub. We will add guidance in the future for how it can be used to edit Video Metadata Hub metadata from within Adobe Premiere Pro.
The IPTC thanks James and his team for their work on the panel and for enhancing it so well over the past 12 months to turn it into a real power tool for media managers who want the full power of IPTC Photo Metadata at their fingertips.
Hands-on metadata workshop in Juan les Pins, France in May 2025
IPTC Managing Director Brendan Quinn will run a workshop on Wednesday 14th May showing users how to activate the plugin and how to use it to edit metadata for various purposes. This workshop will take place as part of the IPTC Day at CEPIC 2025, and so will be accessible to attendees of CEPIC 2025 and of the IPTC 2025 Spring Meeting.
Chinese authorities issued guidelines on Friday requiring labels on all artificial intelligence-generated content circulated online, aiming to combat the misuse of AI and the spread of false information.
The regulations, jointly issued by the Cyberspace Administration of China, the Ministry of Industry and Information Technology, the Ministry of Public Security, and the National Radio and Television Administration, will take effect on Sept 1.
A spokesperson for the Cyberspace Administration said the move aims to “put an end to the misuse of AI generative technologies and the spread of false information.”
According to China Daily, “[t]he guidelines stipulate that content generated or synthesized using AI technologies, including texts, images, audios, videos and virtual scenes, must be labeled both visibly and invisibly” (emphasis added by IPTC). This potentially means that IPTC or another form of embedded metadata must be used, in addition to a visible watermark.
“Content identification numbers”
The article goes on to state that “[t]he guideline requires that implicit labels be added to the metadata of generated content files. These labels should include details about the content’s attributes, the service provider’s name or code, and content identification numbers.”
It is not clear from this article which particular identifiers should be used. There is currently no globally-recognised mechanism to identify individual pieces of content by identification numbers, although IPTC Photo Metadata does allow for image identifiers to be included via the Digital Image GUID property and the Video Metadata Hub Video Identifier field, which is based on Dublin Core’s generic dc:identifier property.
According to the article, “Service providers that disseminate content online must verify that the metadata of the content files contain implicit AIGC labels, and that users have declared the content as AI-generated or synthesized. Prominent labels should also be added around the content to inform users.”
Spain’s equivalent legislation on labelling AI-generated content
The Spanish proposal has been approved by the upper house of parliament but must still be approved by the lower house. The legislation will be enforced by the newly-created Spanish AI supervisory agency AESIA.
If companies do not comply with the proposed Spanish legislation, they could incur fines of up to 35 million euros ($38.2 million) or 7% of their global annual turnover.
On behalf of our memberships, IPTC and PLUS respectfully suggest that existing copyright law is sufficient to enable licensing of content to AI platforms. A “fair use” provision does not cover commercial AI training. Existing United States copyright law should be enforced.
IPTC and PLUS Photo Metadata provide a technical means for expressing the creator’s intent as to whether their creations may be used in generative AI training data sets. This takes the form of metadata embedded in image and video files. This solution, in combination with other solutions such as the Text and Data Mining Reservation Protocol, could take the place of a formal licence agreement between parties, making an opt-in approach technically feasible and scalable.
It is true that our technical solutions would also be relevant if the US government chose to implement an opt-out based approach. However, this does not currently protect owners’ rights well due to the routine activity of “metadata stripping” – removing important rights and accessibility metadata that is embedded in media files, in the misguided belief that it will improve site performance. Metadata stripping is performed by many publishers and publishing systems – often inadvertently.
As a result, we can only recommend that the US adopts an opt-in approach. We request that the US government ensures that metadata embedded in media files be declared as a core part of any technical mechanism to declare content owner’s desire for content to be included or excluded from training data sets.
Content creators are a core part of the US economy and have a strong voice. We agree with their position, but we don’t simply come with another voice of complaint: we bring a viable, ready-made technical solution that can be used today to implement true opt-in data mining permissions and reservations.
Close-up screenshot of Pinterest’s label for AI-generated content.
As reported in Social Media Today, Pinterest has started using IPTC embedded Photo Metadata to signal when content in “Image Pins” has been generated by AI.
Reports started in February that Pinterest had started labelling AI-generated images. Now it has been confirmed via an official update to Pinterest’s user documentation.
In the Pinterest documentation, a new section has recently been added that describes how it works:
Screenshot of Pinterest’s help pages showing how IPTC metadata is used to signal AI-generated content.
“Pinterest may display a label in the foreground of an image Pin when we detect that it has been generated or modified with AI. This is in accordance with IPTC standard for photo metadata. We’re working on ways to expand our capabilities to better identify GenAI content in the future through additional technologies.”
On behalf of our memberships, IPTC and PLUS respectfully suggest that existing UK copyright law is sufficient to enable licensing of content to AI platforms. There is no “fair use” provision in UK copyright law, and “fair dealing” does not cover commercial AI training. Existing copyright law should be enforced.
IPTC and PLUS Photo Metadata provide a technical means for expressing the creator’s intent as to whether their creations may be used in generative AI training data sets. This takes the form of metadata embedded in image and video files. This solution, in combination with other solutions such as the Text and Data Mining Reservation Protocol, could take the place of a formal licence agreement between parties, making an opt-in approach technically feasible and scalable.
It is true that our technical solutions would also be relevant if the UK government chooses to implement an “opt-out” approach similar to that adopted in the EU. However, an opt-out-based approach does not currently protect owners’ rights well, due to the routine activity of “metadata stripping” – removing important rights and accessibility metadata that is embedded in media files, in the misguided belief that it will improve site performance. Metadata stripping is performed by many publishers and publishing systems – often inadvertently. (See our research on metadata stripping by social media platforms from 2019; very little has changed since then)
As a result, we can only recommend that the UK adopts an opt-in approach. We request that the UK ensures metadata embedded in media files be declared as a core part of any technical mechanism to declare content owner’s desire for content to be included or excluded from training data sets.
During the course of this consultation, it has become clear that content creators are a core part of the UK economy and have a strong voice. We agree with their position, but we don’t simply come with another voice of complaint: we bring a viable, ready-made technical solution that can be used today to implement true opt-in data mining permissions and reservations.
At the IPTC Autumn Meeting, the IPTC Standards Committee voted on a change proposed by the Photo Metadata Working Group, which created version 2024.1 of the IPTC Photo Metadata Standard.
The change is minor but important to some: the definition of the Keywords property now includes the following text:
Keywords to express the subject and other aspects of the content of the image. Keywords may be free text and don’t have to be taken from a controlled vocabulary. Codes from the controlled vocabulary IPTC Subject NewsCodes must go to the “Subject Code” field.
This aligns the property definition with the way in which many photo agencies and photographers were already using the field: to convey aspects such as the lighting or lens effects used, “mood” of the image, dominant colour and more.
An image created with Google’s Gemini model. The image contains values for the IPTC Photo Metadata properties Digital Source type (trainedAlgorithmnicMedia) and Credit (“Made with Google AI”).
On Thursday, Google announced that it will be extending its usage of AI content labelled using the IPTC Digital Source Type vocabulary.
In a blog post published on Friday, John Fisher, Engineering Director for Google Photos and Google One posted that “[n]ow we’re taking it a step further, making this information visible alongside information like the file name, location and backup status in the Photos app.”
