Watermarking
Also called Watermarking
Embedding a hidden, detectable signal in AI output so it can later be identified as machine-made.
Think of it like
The faint watermark in a banknote — invisible in normal use, but proof of origin when you hold it to the light.
Example
A text generator subtly biases its word choices in a statistically detectable pattern, so a checking tool can later flag the passage as likely AI-written.
How it actually works
Watermarking aims at provenance: proving what a model produced, to fight deepfakes, misinformation, and undisclosed AI content. In text it nudges token probabilities in a secret pattern; in images it embeds signals in the pixels. The tension is robustness versus imperceptibility — a watermark strong enough to survive edits and paraphrasing tends to be easier to notice or strip. No scheme is yet both invisible and truly tamper-proof.
For product teams
A provenance tool for a world drowning in synthetic media — promising, but not yet a guarantee you can lean on.
For engineers
Embed a detectable statistical signal (token biasing in text, pixel signals in images); robustness to edits trades against imperceptibility.
Related
- Deepfake — A main threat it aims to counter.
- Model Extraction — Can help trace a stolen model’s outputs.
- Responsible AI — Part of responsible deployment.
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