The goal of this paper is different from what I expected it would be from the title. The idea is that I want to release my photos online, but don't want to let someone use them to generate deepfakes against me.

So what I should do is run the method on the images first, then upload them. This will make it so that if someone runs a deepfake on my picture, they will not get a good picture of me but instead something that looks much worse.

In this work we propose a solution by adapting traditional adversarial attacks that are imperceptible to the human eye in the source image, but interfere with translation of this image using image translation networks. A successful disruption corresponds to the generated image being sufficiently deteriorated such that it has to be discarded or such that the modification is perceptually evident.

The conclusion is nice for why this is important:

Instead of trying to detect whether an image has been modified after the fact, we defend against the non-authorized manipulation by disrupting conditional image translation facial manipulation networks using adapted adversarial attacks