> Correctly faking the effects of monitor illumination is a non trivial challenge for well-chosen illumination patterns. Surely not. If you have a face in a s

> Correctly faking the effects of monitor illumination is a non trivial challenge for well-chosen illumination patterns. Surely not. If you have a face in a specific orientation and lighting the whole point of deep fakes is that it generates “style transfer” of the incoming image, including lighting. Doing it in real time is a computational restriction that makes it expensive, not difficult. Are you really suggesting that the deepfake models can’t accurately reproduce structured light and shadow patterns? I find that very surprising, and not what the paper says (“ We verified that the deep fakes created by Avatar- ify (github.com/alievk/avatarify-python) do not incorporate the environmental lighting and are therefore easily identifiable because in the presence of our active illu- mination, their temporal facial hue is flatlined with a nearly zero correlation.” <— they didn’t even test it except on a system that doesn’t do lighting transfer). …so, this feels a lot more like “well, if you’re doing a crappy off the shelf meme using deepfakelive it’s pretty obvious” rather than “detect a convincing DF”. The approach probably doesn’t scale against more sophisticated models or opponents with adequate resources.