Watermark segmentation | Hacker News Reader

Watermark segmentation

github.com

32 pointsabriosi21 comments

Do any of these watermark removal systems support simple "training" on multiple images with identical watermarks? Having multiple example images with consistent watermarks should make removing watermarks much easier than trying to remove one with no context.

I haven't found a tool that implements the techniques described in this Google paper from 8 years ago: https://watermark-cvpr17.github.io/

Honest question, is there even a legitimate use for this specific tech?

It's not illegal to remove watermarks from photos. Ethics is another thing.

Incorrect. Removing the watermark constitutes a derivative work. To distribute this work you need permission from the copyright owner to be legal. This you will almost certainly not get since the point of watermarks is to keep people from stealing copyrighted material.

When in the process of removing the watermark do I distribute the work?

You're correct, making a derivative work isn't automatically "illegal". But what can you do with the de-watermarked image?

You have a picture of yourself or a friend, with a watermark on it. You remove the watermark. Now the picture looks nicer when you look at it. Why's this difficult?

It's difficult because people are trying to figure out where you obtained this hypothetical watermarked image from (that you can't get a non-watermarked version of) and what you plan on doing with it after besides looking at it yourself, and how this becomes something for sale to "enterprise-level clients".

From their website:

> The features your business needs to eliminate watermarks for good.

> Whether you're a photographer or a social media manager, let our AI handle the cleanup so you can focus on creativity.

They seem to think that this is a need businesses have.

The legitimate uses feel kind of rare. Maybe there's some stock photo abandonware out there (questionable "legitimacy", but it's not so out there)? Maybe someone bought stock photos from a company that went bankrupt and never downloaded the non-watermarked version, and somehow that company's IP isn't accessible now? Feels like a stretch.

Upscaling old purchased images feels like a more common need.

Removing the annoying watermarks that some TV stations put in the corner of their shows...

removing the annoying "shot with x phone"

Removing watermarks from works that have since fallen into the public domain, or were to begin with when a service debased it with their logo.

Their first example at https://www.clear.photo/en is absolutely terrible. I assume a showcase would show "good" results, but they display a complete failure.

- Incorrectly identifies areas for inpainting. You can see this with the figure, a lot of detail, not obscured by the watermark, is erased and then redrawn. This leads to a totally distorted look. The belt just disappears into nothing, the cloth just becomes a gradient, where a crisp line used to be.

- Low quality inpainting. Even the inpainting is done terribly. This looks like something done with some very simple diffusion based inpainting. Absolutely not state of the art.

Yeah their approach of using two different models to detect and then inpaint is very subliminal given that many watermarks are semi-transparent. They could have just trained a UNet with adversarial loss + LPIPS to do all the work and it would have worked much better already.

What would be the state of the art?

State of the art is obviously a deep neural network trained for image generation/inpainting. Their inpainting mostly looks like a gradient smeared over the image. Current models can even create fine details and their problem, if anything, is being too detailed.

And some people call generative AI nothing but a copyright laundry…

This is technicaly impressive, but I wonder if this could be put to a use which is generally more constructive. Like maybe removing stains from scans or red eye from pictures.

Look at their first example on: https://www.clear.photo/en

How is this technical impressive? It fails at segmentation and it fails at inpainting.

I presume for a commercial product you would but a successful result front and center.

LOL. They switched out the image on their page. FYI before there was an image of a miniature Baker figurine with chocolate poured down, the Baker figure was totally mangled by the removal process.

Now they replaced it with an image where the inpainting just needs to fill in a gradient. Which is of course trivial.

Why do you not make your product better instead? Obviously the first one was what the customer should expect from your product. Also look at the top left tree! The segmentation still fails to correctly identify the watermark.

I'm surprised there isn't a readily available water-mark remover at this point. A synthetic training set for such a model could be created trivially.

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