A new way to detect ‘deepfake’ picture editing

I'm having trouble following. Can someone explain this in easier terms? What scenario does this address and how does this work exactly?

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.

It's hard to see this as much anything but snake oil. While the technique allegedly allows you to target multiple models, it still requires you to know what adversarial model you're targeting before the fact. If the adversary sees there's visible artifacts they'll just change a few parameters or use a different model and be good to go none the less. In short, while this might be academically interesting, I can't help but feel that this is a futile field to work in. A good way to see why is to look at it this way: a skilled human can manually manipulate an image so that it isn't possible to tell that the image has been manipulated. It is then a matter of teaching the model to do the same, which might take time or be computationally prohibitive for now, but eventually it will be done.

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.

how? Eh, that’s complicated, look up “adversarial neural networks”, there’s a fairly high level overview here: https://towardsdatascience.com/how-to-systematically-fool-an...

While all this stuff around deep fakes and its detection is interesting I'm still left wondering why we don't use cryptography to prove if an image is authentic or not.