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hmm... the big idea that MP3 figured out was you can document all these "if there is a very loud 4000Hz sound, we can't hear a soft 4005Hz sound near it very we

hmm... the big idea that MP3 figured out was you can document all these "if there is a very loud 4000Hz sound, we can't hear a soft 4005Hz sound near it very well" psychoacoustic phenomena and just throw away all that extra "can't hear it very well" information, resulting in a vastly-smaller filesize that still sounds reasonable (yeah yeah it's not FLAC and the purist needs their gold-plated Monster cables, lets not go there, that's not the point) So this attack is kinda a "reverse-MP3" that adds those lossy bits back in, but shaped with an attack payload. Or at least it adds enough pieces of the attack payload that the neural net pattern recognition triggers, while the humans say "Doesn't sound like anything to me". Is that a close-enough explain-like-im-a-freshman?

floatrock 8y ago
Hey Berkeley researchers, if you're reading this and want to make a demo that will really freak people out, embed an alexa activation command into this clip: https://youtu.be/iyXtGo418TY?t=1m11s https://youtu.be/iyXtGo418TY?t=1m11s
jerf 8y ago
I primarily brought up psychoacoustics as an example of the way we don't hear the way microphones do. While you could abuse them, it would be more obvious. In this case what we're getting is the audio equivalent of adversarial examples; see the link I gave for some visual examples. What's interesting there is that they are basically invisible to us, but surprisingly robust. (As another sort of philosophical sidebar, this either proves, or provides very strong evidence, that whatever it is our brains are doing, it is not what deep learning nets are doing, nor anything else vulnerable to such trivial adversarial examples. I've seen adversarial examples against another technique that do seem to work against humans as well, but it requires such a distortion to the image that "I can't tell if that's a dog or a toaster" actually makes sense; it's not just some sort of attack against human vision or something, it's a fancy morphed thing halfway between the two that would probably confuse anything and anybody.)
- floatrock 8y ago
  ah, thanks for the clarification! (and the interesting philosophical sidebar!)

floatrock 8y ago

Hey Berkeley researchers, if you're reading this and want to make a demo that will really freak people out, embed an alexa activation command into this clip: https://youtu.be/iyXtGo418TY?t=1m11s https://youtu.be/iyXtGo418TY?t=1m11s

jerf 8y ago

I primarily brought up psychoacoustics as an example of the way we don't hear the way microphones do. While you could abuse them, it would be more obvious. In this case what we're getting is the audio equivalent of adversarial examples; see the link I gave for some visual examples. What's interesting there is that they are basically invisible to us, but surprisingly robust. (As another sort of philosophical sidebar, this either proves, or provides very strong evidence, that whatever it is our brains are doing, it is not what deep learning nets are doing, nor anything else vulnerable to such trivial adversarial examples. I've seen adversarial examples against another technique that do seem to work against humans as well, but it requires such a distortion to the image that "I can't tell if that's a dog or a toaster" actually makes sense; it's not just some sort of attack against human vision or something, it's a fancy morphed thing halfway between the two that would probably confuse anything and anybody.)

floatrock 8y ago
ah, thanks for the clarification! (and the interesting philosophical sidebar!)