scenario: you want to resell 50000 istock phots, but they are watermarked, making the free high resolution versions valueless.
solution: you use a denoising filter to reconstruct the watermark pixels to plausible original values. Profit!
this: instead of just simple obvious watermarks, you can instead encode visually indistinct fake-noise that deliberately confuses denoising neural networks.
They claim “We find that we can target multiple different models simultaneously with our technique.”, ie. it is reasonably generic.
The OP's title is incorrect. This doesn't detect deepfakes, it serves for people to watermark their images in a way that are hard to remove by conventional ML approaches.
