RU version is available. Content is displayed in original English for accuracy.
Advertisement
Advertisement
⚡ Community Insights
Discussion Sentiment
36% Positive
Analyzed from 988 words in the discussion.
Trending Topics
#text#signatures#generated#more#private#problem#trying#watermarked#anthropic#watermark
Discussion (21 Comments)Read Original on HackerNews
It would look like a lot of little signatures on little bits of text, and then larger signatures on a collection of those chunks once the larger chunk exists. It's not that hard. It's just a lot of signatures.
But this scheme could only ever prove that this bit of text was made by a given AI, and validate anything else ever included in the signature hasn't been tampered with. It's not hard to work up a scheme that proves (within reason) a text was generated no earlier than some date by incorporating some sort of information that could only have been known at that date so that could be validated. But this isn't even a step in the direction of proving that something was made by a human. And that's assuming the private keys stay private, which is its own tricky problem. If a private key ever leaks anything signed with it becomes invalidated.
We don't appear to be talking about cryptographic signing here. (That would never work for the problem because everyone expects unsigned text anyway.) We're talking about:
> It’s basically a text steganography problem (concealing a secret code), made more difficult because the plaintext cannot be arbitrarily manipulated.
As in, trying to force ChatGPT output to contain intentionally crafted ChatGPT-specific LLMisms that a human is unlikely to imitate, even one who reads a lot of ChatGPT output.
But how is this implemented? It's a few lines of code to implement a basic "identify and strip/replace any non printable ascii, unicode ..." or whatever. A screenshot/OCR will also do this.
SO at the end of the day you're left with some dumb rules like "you used `load-bearing` more than once per 500 words, that's AI!"
Currently I can recognize AI text because I read thousands of ai generated text. I know that 110% of yahoo finance news is generated. I don't want to read an AI generated personal blog, but if I do what's the problem really? Other than the companies distinguishing AI text for getting better training data, how do people benefit from watermarked text exactly?
Really, Anthropic (and the other labs that follow) are just trying to satisfy the requirements of the law so they can continue to serve the EU. Wether it's actually effective is something else entirely.
But if we're talking about deterministically taking some watermarked LLM output and having a function removeWatermark(text), it won't necessarily be "trivial" to remove, because the watermark function itself need not be public. Only the API that tests for the watermark need be public, right?
Anthropic's magic watermark could be, like the article mentions, something like "every 7th semicolon has a N% chance to be a comma where N is the sum of the last X characters mod Y, and every character in the bit range q1...q2 has a Z% chance to..." etc etc etc. And if Anthropic controls those variables, it would be very difficult to determine the rule, even with some pretty advanced analysis (I would assume). And keep in mind, that example rule I mentioned is pretty naive, too. I expect the actual rule would be way more advanced and not so straightforward as "swap every <charX> for a <charY>"
For Anthropic, it’s highly likely that the watermark is a SynthID type mark similar to the one that Sean is talking about (I actually ran the analysis here https://johnjwang.com/post/2026/08/12/how-claude-watermarkin...). When we get confirmation of whether all models actually are watermarked, I think we’ll be even more confident.
Of course it’s always possible that Anthropic has come up with a proprietary scheme, but I think it’s definitely harder to implement.
I think the game will be a cat and mouse game similar to LinkedIn and other websites trying to block scrapers: each iteration makes it harder for someone to figure out the watermarking scheme, but likely not impossible
People underestimate the value of rules that only take malice and a little knowledge to break.
just make an API that returns the string distance between a previously generated paragraph and the query?
that would sidestep this whole problem class.
regulators could even specify how that has to work.
what am i missing?
They store some prompts and responses, not all, that's what you're missing.
- watermark-free generation
- the stripping of watermarking from the output of SAAS models
Any discussion of watermarking is dead in the water in a world where we are permitted to have these things. I fear for the future.
Key phrase. And I'm not saying fraud in the legal liability sense. If you're not trying to hide the fact that something was LLM generated, then you have no reason to remove it. If you are trying to hide it, then there's probably a reason, i.e. you would face consequences for doing so, therefore it is fraud.