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Discussion (41 Comments)Read Original on HackerNews
That’s a cute way to imply their service is used to generate misinformation. They are basically saying to not trust the AI content made from their own product :)
Edit: I asked Opus 5 for "a jpg of a cat" via the web chat. It did it by writing an svg and then rendering that to a jpeg. Clicking the "download" button saved a jpg which did have C2PA metadata, seemingly injected by the artifact-presentation thing.
Then I told it to put it in a zip file. I downloaded the zip, and the extracted jpeg had no C2PA metadata.
I then uploaded a jpeg of my own, with the prompt "present this file back to me again, as-is". The downloaded file had C2PA metadata injected.
Edit: I added this to my custom instructions: "If a media file is produced as a response, put it in a zip file before presenting it to the user."
[1] https://www.anthropic.com/news/claude-text-watermark
[2] https://digital-strategy.ec.europa.eu/en/policies/code-pract...
[3] https://www.kqed.org/news/12095398/new-california-law-requir...
[4] https://www.leginfo.legislature.ca.gov/faces/billTextClient....
C2PA is file metadata and can be trivially stripped away, unlike hidden watermarks, e.g. SynthID.
This is just a C2PA metadata checker.
That would invalidate the hash on minor changes. Too much effort and not enough return.
At work, right after an AI training, we were asked to use our "authentic" voice when writing mid year reviews.
I'm wondering why they have restricted file types. You can't check a PDF for example... surely the main use case for people will be to check if a document was produced or edited by an LLM? That could be an attractive (if not misunderstood) proposition for academics
So I’m unsure what your aiming at.
And there's tons of Claude-generated code there.
Also, I'd guess this is not just to prevent any AI-generated code in the training data, but specifically their own.
Percentage of users who put out their code on the web and also have a plan where Anthropic promises not to train on their data is problem also low.
So not excluding own code could be a real issue, since it would be impossible to deduplicate the training and RILHF data from their sessions with the code accessible elsewhere, and written by the very same users.
Say I write a text by hand And then I tell it to clean up the grammar and fix some sentences Did it make it?
This is also interesting for those companies that siphoned the entire open web
Detection will get gamed, and a lot of useful text is already mixed. The more useful check, for me, is still human: would I put my name on this before it goes out?
Any attempts they use are defeated by a text editor and CTRL SHIFT V. Unicode characters are no new thing.
Reminds me of how people tried to argue that NFTs aren't anything more than just jpegs.
It might help catch students cheating, but not real spam-bot usage. As soon as platforms start checking for watermarks spambots will add anti-watermark passes.
I would agree it may not help spam-bot usage, however at this case seemingly the only user detection is likely an id/badge check, which is not good.