You can even describe what you want to generate, and you get it. You get it for images, and you get it for videos. This is really astonishing. There are things I couldn’t believe that could ever happen. Are you aware of the work done by Ilke Demir at Intel with her FakeCatcher? Do you mean the deepfake detector? Yes, I know it. I think they actually were inspired by a paper, if I’m not wrong, on checking about the heartbeat. Yes, this is really interesting. We also worked on these biometric features in order to understand if a video of a person is the real person or not based on these biometrics. This is a very interesting direction. What do we still need to solve in that area? What I think is really challenging is the fact that often, all these videos and images can be of low quality, compressed, and resized. When you upload them over a social network, they can be strongly compressed, so the quality reduces, and also these tiny traces can be reduced… Like artifacts? Yes, these artifacts can be reduced, and it could be harder to detect them. Also, what is really important is to develop explainable detectors so that, as you say, you can look for some specific traces that you can explain. Otherwise, they’re harder to interpret, and you don’t know what’s happening. If the detector says yes or no, why? Can I trust it? This is also very important. It seems like a game of cat and mouse: how to create fakes that are so good that they cannot be detected and how to find them. Who is going to win in the end? This is a really difficult question to answer, but note that even if a fake is perfect visually, this doesn’t mean it doesn’t embed some artifacts inside. It can be a perfect fake, but it can contain some artifacts that can be highlighted by some detectors. The main problem is if you have a very smart, malicious attacker that’s able to hide the traces or even inject some specific traces if it knows the detector you’re using. The problem is when this game is played with people who are also aware of the forensic detectors or have some knowledge so they can actually attack your detector. It seems that, in some way, you believe in the power of your opponents, and they are making your task very difficult. Yes, so you have to take this into consideration when you develop a detector, and you have to try to develop a method that is also robust to possible attacks. You probably do not know the next technique they will develop, but you are confident that you will always find an answer. How can you be so confident? Women in Computer Vision 16 DAILY WACV Saturday
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