7 ms·

Won't this be used in the next deepfake as an adversarial network in order to produce more realistic results? It's an endless cat-and-mouse game.

mumumu 4y ago
This is probably intended for encoding webcam chat between Intel devices. They can hash the video "frames" to detect interception.
AustinDev 4y ago
>It's an endless cat-and-mouse game Yes, this is the way with anything software based that can earn people money. See: video game hacks, SEO manipulation, etc
- deleted 4y ago
  [deleted]
- yreg 4y ago
  But especially so when machine learning is involved since a model can train off its adversary.
  - BoorishBears 4y ago
    Not really special in the case of ML. Before deepfakes, if you wanted to claim a video was doctored in court, you'd find an expert on video editing and have them testify. But the same knowledge that allowed them to identify a doctored video (like 50hz/60hz hum) could be used in an adversarial manner to create a very convincing video. At most deepfakes democratize that "knowledge" in the form of a model, so it still works both ways.
    - JadeNB 4y ago
      > But the same knowledge that allowed them to identify a doctored video (like 50hz/60hz hum) could be in an adversarial manner to create a very convincing video. I don't think it's automatically true that being good at spotting fakes means that you're good at generating fakes. For example, I suspect that I, like most humans, would be pretty good at spotting humanoid robots at the current level of terminology. However, that does not suggest that I would be particularly good at creating humanoid robots that would evade detection.
      - BoorishBears 4y ago
        That's why I specified an example that requires a domain expert, you could have a very poorly done fake video that anyone can tell is fake too.
        JadeNB 4y ago
        > That's why I specified an example that requires a domain expert, you could have a very poorly done fake video that anyone can tell is fake too. But my example also satisfies that criterion. I (and most humans) am a domain expert at identifying humans, but that doesn't mean I would be good at faking a human.
        BoorishBears 4y ago
        I really didn't think I needed to specify domain expert at faking humans. I said video editor not video watcher.
        deleted 4y ago
        [deleted]
        thaumasiotes 4y ago
        > I (and most humans) am a domain expert at identifying humans, but that doesn't mean I would be good at faking a human. I don't think you're thinking about this enough. If you make one effort to create something that looks like a human, you'll do a terrible job unless you are already a skilled artist. But that doesn't support your claims here. We have a scenario where you've prepared a fake human and attempted to pass it off as real. The only way this could actually happen is if you looked at your own handiwork and it passed quality tests. And since you're good at identifying humans, that necessarily means that your fake human is a high-quality imitation. If it were terrible, you'd look at it and realize it couldn't be passed off as real.
        deleted 4y ago
        [deleted]
rogers18445 4y ago
> It's an endless cat-and-mouse game. This is often stated but I think it has to be obviously wrong. This isn't a traditional interactive game such as malware & anti-malware. You have existing sensors which operate under the constraint of [ real world -> theoretic pixel space -> optics & aberrations & sensor noise -> compression ]. And a single adversary which attempts to fake this chain. The detection of fakes isn't even an adversary in this game, it's merely a detection of deviation of the faking process. At some point, probably soon, the faking process will reach a point where any deviation will be drowned out by the noise aspect of optics & sensors & compression.
- halpmeh 4y ago
  One method of generating things via neural networks is called a generative adversarial network. It works by having two models. One that generates content and one that detects fake content. You train them both in parallel. As the fake detector gets better, so does the generative model at generating fakes. It’s literally a cat-and-mouse game. If someone came up with a scheme to reliably detect your fakes, you could add it to your discriminator model and retrain the generator to improve the fake generation.
  - rogers18445 4y ago
    My understanding is that it's not quite that simple. GANs have stability problems (and as a result somewhat out of favor atm) and if the fake detection mechanism isn't a differentiable function itself no training can happen.
    - sigmoid10 4y ago
      The fake detection mechanism (aka discriminator) is usually just another neural network and I bet that's the case here as well. So it must be differentiable and thus, if anyone ever gets a hold of it, it could be easily used to train a generator that will eventually fool the discriminator.
      - 4y ago
Ptchd 4y ago
Just like computer security

mumumu 4y ago

This is probably intended for encoding webcam chat between Intel devices. They can hash the video "frames" to detect interception.

AustinDev 4y ago

>It's an endless cat-and-mouse game Yes, this is the way with anything software based that can earn people money. See: video game hacks, SEO manipulation, etc

deleted 4y ago
[deleted]
yreg 4y ago
But especially so when machine learning is involved since a model can train off its adversary.
- BoorishBears 4y ago
  Not really special in the case of ML. Before deepfakes, if you wanted to claim a video was doctored in court, you'd find an expert on video editing and have them testify. But the same knowledge that allowed them to identify a doctored video (like 50hz/60hz hum) could be used in an adversarial manner to create a very convincing video. At most deepfakes democratize that "knowledge" in the form of a model, so it still works both ways.
  - JadeNB 4y ago
    > But the same knowledge that allowed them to identify a doctored video (like 50hz/60hz hum) could be in an adversarial manner to create a very convincing video. I don't think it's automatically true that being good at spotting fakes means that you're good at generating fakes. For example, I suspect that I, like most humans, would be pretty good at spotting humanoid robots at the current level of terminology. However, that does not suggest that I would be particularly good at creating humanoid robots that would evade detection.
    - BoorishBears 4y ago
      That's why I specified an example that requires a domain expert, you could have a very poorly done fake video that anyone can tell is fake too.
      - JadeNB 4y ago
        > That's why I specified an example that requires a domain expert, you could have a very poorly done fake video that anyone can tell is fake too. But my example also satisfies that criterion. I (and most humans) am a domain expert at identifying humans, but that doesn't mean I would be good at faking a human.
        BoorishBears 4y ago
        I really didn't think I needed to specify domain expert at faking humans. I said video editor not video watcher.
        deleted 4y ago
        [deleted]
        thaumasiotes 4y ago
        > I (and most humans) am a domain expert at identifying humans, but that doesn't mean I would be good at faking a human. I don't think you're thinking about this enough. If you make one effort to create something that looks like a human, you'll do a terrible job unless you are already a skilled artist. But that doesn't support your claims here. We have a scenario where you've prepared a fake human and attempted to pass it off as real. The only way this could actually happen is if you looked at your own handiwork and it passed quality tests. And since you're good at identifying humans, that necessarily means that your fake human is a high-quality imitation. If it were terrible, you'd look at it and realize it couldn't be passed off as real.
        deleted 4y ago
        [deleted]

rogers18445 4y ago

> It's an endless cat-and-mouse game. This is often stated but I think it has to be obviously wrong. This isn't a traditional interactive game such as malware & anti-malware. You have existing sensors which operate under the constraint of [ real world -> theoretic pixel space -> optics & aberrations & sensor noise -> compression ]. And a single adversary which attempts to fake this chain. The detection of fakes isn't even an adversary in this game, it's merely a detection of deviation of the faking process. At some point, probably soon, the faking process will reach a point where any deviation will be drowned out by the noise aspect of optics & sensors & compression.

halpmeh 4y ago
One method of generating things via neural networks is called a generative adversarial network. It works by having two models. One that generates content and one that detects fake content. You train them both in parallel. As the fake detector gets better, so does the generative model at generating fakes. It’s literally a cat-and-mouse game. If someone came up with a scheme to reliably detect your fakes, you could add it to your discriminator model and retrain the generator to improve the fake generation.
- rogers18445 4y ago
  My understanding is that it's not quite that simple. GANs have stability problems (and as a result somewhat out of favor atm) and if the fake detection mechanism isn't a differentiable function itself no training can happen.
  - sigmoid10 4y ago
    The fake detection mechanism (aka discriminator) is usually just another neural network and I bet that's the case here as well. So it must be differentiable and thus, if anyone ever gets a hold of it, it could be easily used to train a generator that will eventually fool the discriminator.
    - 4y ago

Ptchd 4y ago

Just like computer security