The existence of GANs as a mechanism for learning is reason enough to err away from static detection methods.
To uncover a deepfake video call, ask the caller to turn sideways
251–260 of 285 posts
Re: To uncover a deepfake video call, ask the caller to turn sideways
#252The existence of GANs as a mechanism for learning is reason enough to err away from static detection methods.
We can already resolve the interframe wobbles that happen due to the inability to preserve spatial invariants on static meshes allowing for dynamic camera motion.
Of course, the spatial invariants of meat-suits in motion require an understanding of volumetric structure, and not just restricted depth surface meshes.
But it's not some unencodable computational enigma.
Re: To uncover a deepfake video call, ask the caller to turn sideways
#253They can also partially occlude their face too.
Re: To uncover a deepfake video call, ask the caller to turn sideways
#254I was recently looking for designers for my company when I came across an interesting profile on Dribbble. I reached out and quickly scheduled a time when we could talk over zoom. At the meeting time, in comes this person who seems to have a strange-looking, silicone-like face. I was using my Zoom account (I rarely use other peoples zooms unless I trust them), to avoid situations like this. One thing I noticed is tha…
I do very much hope that you told the candidate what spooked you. Ideally, you would have done this early in the interview, giving them a chance to disable any video filtering / face-beautifying software that they may have been running.
If you didn't do either one of those, perhaps you now know enough so that next time you will be able to give the interviewee a chance to demonstrate whether or not they're using a "Smooth over my facial blemishes because I'm uncomfortable with how my face looks and want it to look 'prettier'." filter.
Best of luck with your interviews!
Re: To uncover a deepfake video call, ask the caller to turn sideways
#255Source: I work in the field. This is a current limitation, and an artifact of the data+method but not something that should be relied upon. If we do some adversary modelling, we can find two ways to work around this: 1) actively generate and search for such data; perhaps expensive for small actors but not well equipped malicious ones. 2) wait for deep learning to catch up, e.g. by extending NERFs (neural radiance fie…
> This is a current limitation The thing with any AI/ML tech is that current limitations are always underplayed by proponents. Self-driving cars will come out next year, every year. I'd say that until the tech actually exists, this is a great way to detect live deepfakes. Not using the technique just because maybe sometime in the future it won't work isn't very sound. For an extreme opponent you may need additional s…
I think it's odd we don't think of other limitations of products the same way. Put another way, why don't we just say it can't do it.
Example, we don't say a jet ski has a current speed limitation of 80 mph, we say it can go 80, but not 81. It's a simple fact. No promise that it will be faster tomorrow, because that's not what it is, it's not its future self.
It's like they're combining startup it will always be better after you invest more money with the reality of what "is" means.
Re: To uncover a deepfake video call, ask the caller to turn sideways
#256Earlier quoted context omitted.
The solution you propose sounds vastly overengineered. Why would we need remote attestation, tampering resistance and enclaves when this is simply a problem of your peers being unauthenticated? If you care about the identity of who you are speaking to remotely, the only solution is to cryptographically verify the other end, which just requires plain old key distribution and verification. It's just not widespread enou…
How do you verify they are who they say they are, though? And verifying their picture matches their name?
you don't. You assume that the key distribution mechanism is secure, so that whoever has the key _is_ who they say they are.
This doesn't prevent adversarial impersonation (where you cannot trust the party that want themselves impersonated). E.g., if you are an employer, and interviewing via video, you cannot tell that the person authenticated and performing on video is indeed the person you're hiring. I dont think this is a problem that _should_ be solved tbh.
Re: To uncover a deepfake video call, ask the caller to turn sideways
#257Source: I work in the field. This is a current limitation, and an artifact of the data+method but not something that should be relied upon. If we do some adversary modelling, we can find two ways to work around this: 1) actively generate and search for such data; perhaps expensive for small actors but not well equipped malicious ones. 2) wait for deep learning to catch up, e.g. by extending NERFs (neural radiance fie…
> This is a current limitation The thing with any AI/ML tech is that current limitations are always underplayed by proponents. Self-driving cars will come out next year, every year. I'd say that until the tech actually exists, this is a great way to detect live deepfakes. Not using the technique just because maybe sometime in the future it won't work isn't very sound. For an extreme opponent you may need additional s…
> The thing with any AI/ML tech is that current limitations are always underplayed by proponents
if you don't worry about deepfakes, ok. But if you worry about deepfakes, you should not be reassured that this glitch is going to save you.
I'm not a proponent, just think your argument in this context doesn't work.
Re: To uncover a deepfake video call, ask the caller to turn sideways
#258Source: I work in the field. This is a current limitation, and an artifact of the data+method but not something that should be relied upon. If we do some adversary modelling, we can find two ways to work around this: 1) actively generate and search for such data; perhaps expensive for small actors but not well equipped malicious ones. 2) wait for deep learning to catch up, e.g. by extending NERFs (neural radiance fie…
> This is a current limitation The thing with any AI/ML tech is that current limitations are always underplayed by proponents. Self-driving cars will come out next year, every year. I'd say that until the tech actually exists, this is a great way to detect live deepfakes. Not using the technique just because maybe sometime in the future it won't work isn't very sound. For an extreme opponent you may need additional s…
The self driving car of next year arrives just in time for the Iranian atomic bomb :-D
which is ready in two years for about as long as I'm around. https://www.theatlantic.com/international/archive/2015/04/ir...
If all the money on self driving cars would have been put into public transport (driverless on rails is a solved issue) and pushing shared car ownership instead, we might actually get somewhere towards congestion-free cities.
Re: To uncover a deepfake video call, ask the caller to turn sideways
#259Earlier quoted context omitted.
The only person who is promising self driving cars next year (and has done so every year for the past 5 years) is Elon Musk. Most respectable self-driving car companies are both further along than Tesla and more realistic about their timelines.
Let's not dismiss the point that self-driving cars are the "stone soup" of machine learning industry. Like the monk who claimed he could make soup with just a stone, machine learning claimed that with two cameras, two microphones, and steering/brake/accelerator control, a machine would someday soon drive just like a human can with that hardware equivalent. Then it turned out well, we actually need a lot more cameras.…
"Machine learning has utterly failed to deliver on this original promise of learning to operate a vehicle like a person, with no more sensors than a person."
I am not aware of anyone except Musk making that claim. "Machine learning" as in the statements of the main researchers, certainly did not promise anything like it.