CAI Achieves Milestone: White Paper Sets the Standard for Content Attribution
by Andy Parsons
posted on 08-03-2020
Today marks a significant milestone for the Content Authenticity Initiative (“CAI”) as we publish our white paper, “Setting the Standard for Content Attribution”. It addresses the mounting challenges of inauthentic media and our proposal for an industry-standard content attribution solution that will enable creators to securely attach their identity and other information to their work before they share it with the world.
Addressing a growing problem****
The need to combat intentionally deceptive content has never been more urgent. According to a 2019 study by the Pew Research Center, nearly two-thirds of Americans say that synthetic or altered images and videos create confusion about the facts of current issues and events. In recent months, social media sites and news organizations have begun applying “manipulated media” tags to doctored images and videos that are meant to mislead or stoke division among the public. The challenge is only growing as the volume of inauthentic media increases.
Efforts to address content authenticity have largely focused on using AI to detect deep fakes and other altered media. And, that effort is important. But shouldn’t there also be a transparent way to inform the public who created the original photos and videos, and how these assets were changed over time? Isn’t it equally important that creative professionals and photojournalists receive credit for their work?
That’s exactly the goal of the CAI. As explained in our white paper, we propose adding a layer of secure provenance which expresses relevant facts about how media is altered from the moment of creation to the moment of audience experience. This technique will eliminate much of the uncertainty currently facing editors and authors of creative content and provide greater transparency into the origins of online media for consumers.
How attribution works
For the last nine months, the CAI has been hard at work with a diverse interdisciplinary group of collaborators, including industry leaders from technology, media, academia, advocacy groups, government and think tanks. Our mission is to develop an open, extensible attribution solution that can be implemented across devices, software, publishing and media platforms.
Today, attribution information is typically embedded in the metadata of digital assets, making it corruptible and untrustworthy. Even when careful steps are taken to verify metadata in production workflows, most assets appear online without the information intact, leaving content moderators and fact-checkers to reconstruct the provenance and context of content through imperfect methods. CAI data, in contrast, is cryptographically sealed and verifiable by an individual or organization along the path from creation to consumption. ****
We believe the CAI solution described in the paper achieves a critical balance of security, resilience, privacy and interoperability. At its core, CAI attribution introduces constructs called assertions and claims. Simply put, assertions capture the who, what, and how of asset creation and modification while claims add a layer of cryptographic verifiability and trust. In combination with a growing set of supported file formats, and the fundamental principle that CAI data can be stored in files or linked in the cloud, assertions and claims can become the lingua franca of secured metadata for systems from mobile devices to social media.
At the same time, we pay careful attention to situations where full transparency is not prudent. For cases where anonymity is essential and revealing too much detail can cause harm, the CAI system supports intentional use through opt-in parameters and redaction of prior assertions when warranted. Critically, embracing this functionality allows users to share only the details they wish to share without compromising the ability to trace provenance downstream.**
Our white paper also outlines some CAI-enabled use cases, which demonstrate the essential characteristics of the attribution system we are designing. We imagine the system in the hands of creative professionals, photojournalists and human rights activists who may benefit from the promising potential CAI offers. There are many more scenarios to explore and we intend to do that through continued collaboration with stakeholders.
With widespread adoption of CAI’s attribution specifications, we hope to significantly increase transparency in online media, provide consumers with a way to decide who and what to trust and create an ecosystem that rewards impactful, creative work.
We’ve reached this goal through the generous participation of the co-authors and collaborators who represent a diverse set of viewpoints. I would like to acknowledge the time and thought contributed by the CAI collaborators. Without their expertise and vision this milestone could not have been reached. The full list of authors and collaborators is below.
Andy Parsons (Adobe)
Leonard Rosenthol (Adobe)
Eric Scouten (Adobe)
Jatin Aythora (The British Broadcasting Corporation)
Bruce MacCormack (CBC/Radio-Canada)
Paul England (Microsoft Corporation)
Marc Levallee (The New York Times Company)
Jonathan Dotan (Stanford Center for Blockchain Research)
Sherif Hanna (Truepic)
Hany Farid (University of California, Berkeley)
Sam Gregory (WITNESS)
Will Allen (Adobe)
Pia Blumenthal (Adobe)
John Collomosse (Adobe)
Oliver Goldman (Adobe)
Andrew Kaback (Adobe)
Gavin Peacock (Adobe)
Charlie Halford (The British Broadcasting Corporation)
Scott Lowenstein (The New York Times Company)
Thomas Zeng (Truepic)
Fabiana Meira Pires De Azevedo (Twitter)
Corin Faife (WITNESS)
Our work continues
Today’s white paper is the result of many hours of problem definition, system design, use case exploration and vigorous debate with multiple stakeholders groups culminating in the ideas expressed in the document. At the same time, we also humbly embrace the magnitude of our mission in knowing that this is only a first step; we’ve arrived at the starting line of a long journey. As we share our work, we are advancing on the path toward an industry standard for digital content attribution. And with this first step, we look optimistically toward a future with more trust and transparency in media.
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Topics: Art, Digital Transformation, Content Management