scenario: you want to resell 50000 istock phots, but they are watermarked, making the free high resolution versions valueless. solution: you use a denoising fi

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-image-recognition-neural-network-7b2ac157375d https://towardsdatascience.com/how-to-systematically-fool-an...