DeepFake Detector AIs Are Good Too!

DeepFake Detector AIs Are Good Too!


Dear Fellow Scholars, this is Two Minute Papers
with Károly Zsolnai-Fehér. We talked about a technique by the name Face2Face
back in 2016, approximately 300 videos ago. It was able to take a video of us and transfer
our gestures to a target subject. With techniques like this, it’s now easier
and cheaper than ever to create these deepfake videos of a target subject provided that we
have enough training data, which is almost certainly the case for people who are the
most high-value targets for these kinds of operations. Look here. Some of these videos are real, and some are
fake. What do you think, which is which? Well, here are the results – this one contains
artifacts and is hence easy to spot, but the rest…it’s tough. And it’s getting tougher by the day. How many did you get right? Make sure to leave a comment below. However, don’t despair, it’s not all doom
and gloom. Approximately a year ago, in came FaceForensics,
a paper that contains a large dataset of original and manipulated video pairs. As this offered a ton of training data for
real and forged videos, it became possible to train a deepfake detector. You can see it here in action as these green
to red colors showcase regions that the AI correctly thinks were tampered with. However, this followup paper by the name FaceForensics++
contains not only not only an improved dataset, but provides many more valuable insights to
help us detect these DeepFake videos, and even more. Let’s dive in. Key insight number one. As you’ve seen a minute ago, many of these
DeepFake AIs introduce imperfections, or in other words, artifacts to the video. However, most videos that we watch on the
internet are compressed, and the compression procedure…you have guessed right, also introduces
artifacts to the video. From this, it follows that hiding these DeepFake
artifacts behind compressed videos sounds like a good strategy to fool humans and detector
neural networks likewise, and not only that, but the paper also shows us by how much exactly. Here you see a table where each row shows
the detection accuracy of previous techniques and a new proposed one, and the most interesting
part is how this accuracy drops when we go from HQ to LQ, or in other words, from a high-quality
video to a lower-quality one with more compression artifacts. Overall, we can get an 80-95% success rate,
which is absolutely amazing. But, of course, you ask, amazing compared
to what? Onwards to insight number two. This chart shows how humans fared in DeepFake
detection, as you can see, not too well. Don’t forget, the 50% line means that the
human guesses were as good as a coinflip, which means that they were not doing well
at all. Face2face hovers around this ratio, and if
you look at NeuralTextures, you see that this is a technique that is extremely effective
at fooling humans. And wait…what’s that? For all the other techniques, we see that
the grey bars are shorter, meaning that it’s more difficult to find out if a video is a
DeepFake because its own artifacts are hidden behind the compression artifacts. But the opposite is the case for NeuralTextures,
perhaps because its small footprint on the videos. Note that a state of the art detector AI,
for instance, the one proposed in this paper does way better than these 204 human participants. This work does not only introduce a dataset,
these cool insights, but also introduces a detector neural network. Now, hold on to your papers because this detection
pipeline is not only so powerful that it practically eats compressed DeepFakes for breakfast, but
it even tells us with remarkable accuracy which method was used to tamper with the input
footage. Bravo! Now, it is of utmost importance that we let
the people know about the existence of these techniques, this is what I am trying to accomplish
with this video. But that’s not enough, so I also went to
this year’s biggest NATO conference and made sure that political and military decision
makers are also informed about this topic. Last year, I went to the European Political
Strategy Center with a similar goal. I was so nervous before both of these talks
and spent a long time rehearsing them, which delayed a few videos here on the channel. However, because of your support on Patreon,
I am in a fortunate situation where I can focus on doing what is right and what is the
best is for all of us, and not worry about the financials all the time. I am really grateful for that, it really is
a true privilege. Thank you. If you wish to support us, make sure to click
the Patreon link in the video description. Thanks for watching and for your generous
support, and I’ll see you next time!

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