The exact method for watermarking AI output will depend on the type of media being generated, but the principle is generally the same: The generated media is embedded with data that is undetectable (or occasionally detectable) to the human eye or ear, but which identifies it as AI-produced. Watermark-detecting tools can then be used to identify if something was made with AI, without guessing or using an unreliable AI “detector”. Here are some examples of how watermarks may be implemented for various media types.
For audio: Embedding a watermark in audio files can be even easier, placing signature sounds outside the range of human hearing (typically below 20Hz or above 20,000Hz). SynthID has an audio component that can be heard by watermark detectors, but remains imperceptible to the ear.
For text: Every next word an LLM generates comes with a probability score. The sentence “The cat is” could end with “fluffy,” “cute,” or “small.” Each one of those words is given a percentage likelihood that it will appear. Text watermarks work by inflating the chances that certain sets of random words will appear. This can make it possible to detect LLM-generated text without changing its semantic meaning. However, this tends to work better for longer pieces of text, which provides more chances to detect the presence of less-likely words.
While SynthID originated with Google’s DeepMind, the company open-sourced the protocol, and now it’s also used by other AI companies, including OpenAI. Some companies. conversely, do not use a watermark at all—and among those that do, the implementation can be inconsistent between tools. In other words, the presence of an AI watermark can confirm something was made or modified with AI, but the absence of one can’t prove it wasn’t.
How can you detect AI watermarks?
However, these tools have limitations. OpenAI’s detector, for some reason, only seems to detect media generated by OpenAI itself. During my testing, I tried uploading images generated via Gemini—which could detect its own SynthID watermark—and OpenAI’s tool did not find it.
Even if you do your diligence to check for every version of a SynthID watermark or C2PA metadata, it’s still possible that a piece of media could have some other form of watermark that requires a different detector. Unfortunately, unless you have a strong indicator of which tool was used to create a piece of generated media, it can still be difficult to thoroughly check for every kind of watermark.
With enough work, any watermark can technically be removed, though SynthID specifically is pretty resistant to most typical forms of modification. An AI-generated image with a SynthID watermark that has been cropped, filtered, or modified can still retain enough of its original watermark to be detectable. It’s not impossible to remove, but it’s generally hard to do so accidentally.
That means that if you want to maintain a metadata chain that lets you prove the authenticity of an image, it’s important to download or upload the specific original files and make sure any editing tools you use support maintaining that metadata.
Does a watermark always mean a piece of media was AI-generated?
It’s important to keep in mind that a watermark can be added to an authentic piece of media. If someone uploads an authentic photo to a tool like Gemini to perform simple edits, the output image will have a SynthID watermark, too. This doesn’t mean the whole image is inauthentic, but it will still be flagged by watermark detectors.
As mentioned before, the absence of a watermark cannot prove that an image is authentic. Even the presence of a watermark can’t prove that the substance of an image isn’t real. Watermarks and metadata are simply tools to help you figure out where a piece of media likely came from and how it might’ve been modified. Ultimately, it’s still up to you to verify the things you see and hear online.
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