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Comment by FartyMcFarter | Hacker News Reader

Think about watermarks in images. They can be removed, but it isn't easy to do so without making it obvious that the image is damaged / doctored.

I imagine this is also the case for audio watermarks, but I'm not sure what the current state of the art is for watermark removal.

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> They can be removed, but it isn't easy to do so without making it obvious that the image is damaged / doctored.

This is primarily because those watermarks damage the image. The information about the content of the image beneath the watermark is missing (replaced with the watermark) and would have to be extrapolated or fabricated to replace it.

If the watermark isn't visible as part of the image then there is no missing piece to have to replace.

Modern content-aware fill can very impressively replace dead areas of an image. E.g. in Photoshop 2024

That isn't reconstructing what was lost. It was lost. Photoshop is machine learning taking a guess at what it was and putting that there. Sometimes it's pretty close. Sometimes it's not.

For this application you don't need to reconstruct what was lost. You merely need to fill in some plausible content that is not identified as a particular watermark.

That may work where you have a visible visual watermark over part of an image, but not so much if steganography is used to add an invisible watermark.

Well at least we can damage the watermark such that it’s no longer machine readable. Since according to its marketing it’s invisible to listeners, our distortion of it should be equally invisible.

You can easily train a model to clean watermark from images.

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