Google DeepMind reference code for watermarking text from language models and detecting that watermark.
Reference code from a Nature paper, with frequentist and Bayesian watermark detectors.
SynthID Text is Google DeepMind's reference implementation of its text watermarking and detection method. It suits researchers who generate text with their own models and want to verify it later, not people checking unknown text.
A Jupyter notebook extends Hugging Face Gemma 3 and GPT-2 models so they watermark text as they generate, then detects the watermark with a frequentist detector (an exact binomial p-value) or a trainable Bayesian detector.
It suits researchers and developers who control the generating model. It cannot tell you whether arbitrary text came from ChatGPT or Claude, because it detects only text carrying its own watermark, and the repo says it is not for production.
Adds watermarking to Gemma 3 and GPT-2 models in PyTorch.
Needs no training and returns an exact binomial p-value for a given key.
A trainable detector the README calls more powerful, scored with JAX on CPU.
Runs in Colab or locally with Gemma 3 270M IT or GPT-2.
The core library is distributed on PyPI for the notebook.
Implements the method from the published Nature paper.
SynthID Text is free under the Apache-2.0 licence. The repo shows no paid plan; the only cost is running the notebook yourself, for example on a free Colab runtime.
880 open remote AI-training roles, updated November 15, 2026; most pay $20–$175 an hour. Zekai lists them free; the application goes to the employer’s platform.
These reviewed AI tools do the same job instantly — from our scored directory.
No. It detects only text generated with its own watermark under a key you hold.
The README says the implementation is for reference and research reproducibility and is not designed for production systems.
The README says any GPU such as a T4 works for Gemma 3 270M IT, and GPT-2 runs on any runtime, faster on a high-RAM CPU or GPU.
See Zekai first in Google