SynthID – A tool to watermark and identify content generated through AI

1. One-sample detection is impossible. These detection methods work at the distributional level—more like a two-sample test in statistics—which means you need to collect a large amount of generated text from the same model to make the test significant. Detecting based on a short piece of generated text is theoretically impossible. For example, imagine two different Gaussian distributions: you can never be 100% certain whether a single sample comes from one Gaussian or the other, since both share the same support.

2. Adding watermarks may reduce the ability of an LLM, which is why I don’t think they will be widely adopted.

3. Consider this simple task: ask an LLM to repeat exactly what you said. Is the resulting text authored by you, or by the AI?

For images/video/audio, removing such a watermark is very simple. By adding noise to the generated image and then using an open-source diffusion model to denoise it, the watermark can be broken. Or in an autoregressive model, use an open-sourced model to do generation with "teacher forcing" loll.

UMG (music label) has been watermarking their music for many years now, and I'm unaware of any tool to remove their watermarks.