为减少视觉错误信息，Adobe 推出了开源工具包
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Adobe 设想的是为网络上充斥的照片和视频标注关于它们的来源。该公司的主要目标是减少视觉错误信息的传播，不过，该系统也可以使那些“希望将自己的名字与工作关联起来”的内容创作者受益。


Adobe 在 2019 年首次宣布了其 <ruby> 内容真实性计划 <rt>  Content Authenticity Initiative </rt></ruby>（CAI）项目，此后，它发布了一份关于实现该目标的技术白皮书，将该系统集成了到自己的软件中，并与新闻编辑室和硬件制造商展开了合作，以帮助普及其愿景。


现在，该公司发布了一个由三部分组成的开源工具包，从而把该技术交到开发人员手中，并投入使用。Adobe 的新开源工具包括一个用于开发“在浏览器中显示内容凭据”的 JavaScript SDK、一个命令行实用程序，和一个用于开发桌面应用程序、移动应用程序和其他应用的 Rust SDK，以创建、查看和验证嵌入式内容凭据。


众所周知，照片的 EXIF 数据中记录了有关光圈和快门速度的信息，这个新标准也采用了这种方式，它还记录有关文件创建的信息，例如文件的创建和编辑方式。如果该公司的共同愿景成真，这些 Adobe 称之为“内容凭证”的元数据，将在社交媒体平台、图像搜索平台、图像编辑器、搜索引擎中广泛可见。


C2PA 是 Adob​​e 的 CAI 与 微软、索尼、英特尔、推特以及 BBC 等合作伙伴的合作成果。华尔街日报、尼康和美联社最近也加入了 Adob​​e 的这个计划，即将 <ruby> 内容认证技术 <rt>  content authentication </rt></ruby> 更加广泛地应用。


有了这些新工具，社交媒体平台就可以使用 Adob​​e 的 JavaScript SDK，快速让平台上的所有图像和视频显示内容凭据，这些凭据将会在鼠标悬停时，显示为右上角的一个图标。因此，无需专门的团队和更大的软件构建，该实施可以由几个开发人员在几周内完成。


CAI 的主要目标是打击互联网上的视觉错误信息，比如那些扭曲乌克兰战争的旧图片的重新传播，或是臭名昭著的南希·佩洛西的“廉价假货”。不过，数字监管链也可能使“作品被盗或出售”的内容创作者受益，这个问题多年来一直困扰着视觉艺术家，现在也正在 NFT 市场引发问题。


根据 Parsons 的说法，CAI 还引起了那些“制作合成图像和视频”的公司的巨大兴趣。公司可以将原始元数据嵌入到我们从 DALL-E 等模型中看到的那种 AI 创作中，从而确保它提供的合成图像不会轻易被误认为是真实的东西。




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via: <https://www.opensourceforu.com/2022/06/adobe-launches-open-source-toolkit-to-contain-visual-misinformation/>


作者：[Laveesh Kocher](https://www.opensourceforu.com/author/laveesh-kocher/) 选题：[lkxed](https://github.com/lkxed) 译者：[lkxed](https://github.com/lkxed) 校对：[wxy](https://github.com/wxy)


本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出


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Adobe envisions a web littered with photos and videos labelled with information about where they came from. The company’s primary goal is to reduce the spread of visual misinformation, but the system could also benefit content creators who want to keep their names associated with their work.

Adobe’s Content Authenticity Initiative (CAI) project, first announced in 2019, has since released a whitepaper on a technology to do just that, integrated the system into its own software, and partnered with newsrooms and hardware makers to help universalize its vision.

The company is now announcing the release of a three-part open source toolkit to get the technology into the hands of developers and out into the wild. Adobe’s new open source tools include a JavaScript SDK for developing ways to display content credentials in browsers, a command line utility, and a Rust SDK for developing desktop apps, mobile apps, and other experiences for creating, viewing, and verifying embedded content credentials.

In the same way that EXIF data stores information about aperture and shutter speed, the new standard also records information about a file’s creation, such as how it was created and edited. And if the company’s shared vision comes true, that metadata, which Adobe refers to as “content credentials,” will be widely viewable across social media platforms, image search platforms, image editors, search engines.

C2PA is the result of a collaboration between Adobe’s CAI and partners such as Microsoft, Sony, Intel, Twitter, and the BBC. The Wall Street Journal, Nikon, and the Associated Press have recently joined Adobe’s pledge to make content authentication more widely available.

With the new tools, a social media platform could use Adobe’s JavaScript to quickly have all of its images and videos display the content credentials, which appear as a mouse-over icon in the upper-right corner. Instead of requiring a dedicated team and a larger software buildout, that implementation could be completed in a few weeks by a couple of developers.

The CAI’s primary goal is to combat visual misinformation on the internet, such as recirculated old images distorting the Ukrainian war or the infamous Nancy Pelosi “cheapfake.” However, a digital chain of custody could also benefit content creators who have had their work stolen or sold, a problem that has plagued visual artists for years and is now causing problems in NFT markets.

According to Parsons, the CAI is also attracting a surprising amount of interest from companies that create synthetic images and videos. Companies can ensure that generative images aren’t easily mistaken for the real thing by embedding origin metadata into the kind of AI creations we’re seeing from models like DALL-E.