There is a program built into Whonix, I believe it's called Kloak, that randomizes your keyboard input times so you can't be identified via keystroke timing JavaScript. There's also research into defeating stylomeyric analysis such as anonymouth but I'm sure there are plenty of new tools, if anyone find any that work well please reply here as I haven't looked in some years. 'Stylometric analysis' is the key phrase to search for.

With AI this will get worse (better identification based on typing styles) but it will also get better because you can setup a local LLM and ask it to re-write your text in a certain style. Touching on this, everyone uses a combination of unique phrases and misspelling or mis-spelling (see?) of words, and with enough text from a given account the chance of statistical probability in attribution is very high. It's how the Unibomber was identified after his manifesto was published, because he used a very specific phrase incorrectly and his brother recognized it, so his wife convinced him to call the FBI tip line about his brother.

Yeah, it would need to be a browser extension, adding a button to scramble every text input field. Or maybe even on the OS level, opening an input field above the browser one, so the original text was never input into the browser.

Translate to some foreign language. Then translate to some other foreign language. Then translate back to your language. Congrats, your writing style changed.

My coworkers use chatgpt for this. Since it always answers in the same generic ways it's helpful to anonymize their peer reviews.

You can look here, maybe you find something usefull, if you search for AI (+150k apps)

• https://www.futuretools.io
• https://www.futurepedia.io
• https://huggingface.co/spaces

I wouldn't just trust random Lemmy users (no offense) but instead check for actual fields, e.g stylometry or writeprint, and from there check the state of the art. Not being an expert would make that tricky so I would take a recent published papers, e.g https://arxiv.org/abs/2203.11849 to understand the challenge. As is always the case they'll review the field, e.g section 2 here, and clarify the 2 sides of the arm race, here Obfuscation/Deobfuscation. The former in 3.2 mentions examples of techniques authors estimate to be good starting point, e.g writeprintsRFC. I'd then search for such tools if they don't directly provide link to open-source repository, e.g theirs https://github.com/reginazhai/Authorship-Deobfuscation . I would then try a recent one that I can easily setup, e.g via Docker, and give it a go. I would then read the rest of the paper, see who cites it, and try to get a more up to date version.

TL;DR: I don't know but there is dedicated research which result I'd trust more than the opinion of strangers who are probably not expert.

Just ask ChatGPT to paraphrase.

I wonder if google translate through multiple languages can fm the trick?

There was a talk about detecting patterns and writing styles at Chaos Computer Congress a bunch of years ago.

The researchers also presented a tool to anonymize text as far as I can remember.

I will go look for the talk.

Edit: Found it!

https://media.ccc.de/v/31c3_-_6173_-_en_-_saal_g_-_201412291715_-_source_code_and_cross-domain_authorship_attribution_-_aylin_-_greenie_-_rebekah_overdorf

They talk about their software to find who wrote what, but also how to use that knowledge to write software that attempts to anonymize text.

Non-native english speakers tend to mix up various styles, you could ask somone to paraphrase your text.

Probably the cut and paste from magazines, but you copy and paste the sentences you want to use. A lot of extra work, but no AI to rat you out.

US to UK English and vice-versa.

Autocorrect?

If you use it before it has learned you writing idiosyncrasies?

ChatGPT will probably remember it was you who asked and doxx you in retaliation when it discovers you’ve plagerized chatGPT.

Another thought is to translate it into Scottish. But then again, you probably still want to be understood.

Changing dialect may be too small of a change. But if you could say write this like 1-2 generations younger/older using high school slang of the time you might get a useful difference.

I had asked for the same thing a while back but didn't really get much. The round-about method that I have found is to finetune FOSS LLMs on data you want it to represent (largely text) and then diving into some prompt engineering to get it to say something you like.

However, I haven't been able to find a test which can accurately point towards text not having specific weights that it relies on. Cue the attacks on GPT-4 which deanonymises data it was trained on. You might also want to read about DPT and Shadowing techniques to red-team LLMs and LLM-generated text as literature.

Cheers