AI watermarking
Techniques that embed a detectable signal in AI-generated text, images, audio, or video so downstream systems can identify content as machine-generated.
Text watermarking modifies the model's sampling distribution in a way that is statistically detectable later but invisible to human readers. Image and video watermarks add cryptographic patterns into pixel data. Audio watermarks ride in inaudible frequencies.
In 2026, watermarking is mandatory for some media output in the EU (AI Act Article 50) and is a voluntary norm at most frontier labs. Google's SynthID covers Gemini outputs across modalities; OpenAI watermarks DALL-E and Sora content.
Watermarks are statistical signals, not guarantees. Adversaries can erase them with enough effort (paraphrasing for text, recompression for media). They raise the cost of large-scale misuse without preventing determined attacks.
Frequently asked
Can I detect AI-generated text reliably in 2026?+
Not 100%. Watermarked outputs from cooperating labs are detectable; non-cooperating or paraphrased outputs are not. Classifier-based detectors (no watermark needed) hover at 70–90% accuracy with meaningful false-positive rates.