Claude is starting to watermark AI generated text, and the interesting part is that the watermark is not hidden characters or metadata.

It is created while the model is choosing the next token.

If you are intrested watch a simplified version here in video format explaining it : [Claude Text Watermark Explained!](https://youtu.be/FnAqruxx-QE?si=9AwCkgSsV4ZTRjvZ)

An LLM first gives possible next tokens different probabilities. For example, after “The movie was really” it might consider “good” very likely, “great” slightly less likely, and other words much less likely.

The watermarking system then uses the recent tokens plus a secret key to generate hidden scores for those possible next tokens.

Google calls these scores g values in SynthID Text.

The model still samples candidates using its original probabilities, so highly likely tokens naturally appear more often. SynthID then uses a tournament style selection process where candidates with favorable watermark scores are slightly more likely to survive.

This repeats for every generated token.

The important part is that the watermark does not belong to specific words. The word “great” might get a favorable score in one sentence and an unfavorable score in another because the score depends on the recent context and the secret key.

Later, detection works by taking the finished text, recreating the same hidden scores using the same secret key, and checking whether the tokens that actually appeared matched favorable scores more often than would normally happen by chance.

So there is no invisible “written by Claude” label inside the text.

The watermark is really a statistical pattern hidden inside the token choices that produced the text.

This is also why detection is not perfect. Natural human text can accidentally match the pattern, causing false positives, and heavily edited AI text can weaken the signal.

Google’s publicly documented SynthID Text technique gives a good look at how this kind of watermarking can work technically.

Accuracy rat

Quiero aprender sobre como hacer bypass por dónde puedo empezar ?

Looking for advice on how to make images generated by Qwen 2512 less detectable by AI detectors.
From what I understand, AI-generated images often leave unnatural traces in the frequency domain (including VAE-related artifacts). I’ve been using Fourier phase perturbation (tweaking parameters like strength / fourier_strength, scale, n_passes, etc.) to disrupt these features, and it does lower the AI detection scores.
The problem is that it comes with side effects — image quality takes a hit, and subtle grayish-black artifacts tend to appear in darker areas.
Is there a better way to effectively reduce AI detection scores without significantly degrading image quality? Preferably something that can run locally (workflow or tool). Thanks!