Re:Adversarial Examples That Fool Both Computer Vision and T...
by saagarjha on Monday November 12, 2018 @08:53AM
(#18432569)
I’m no machine learning expert, but it seems to me that neural networks just don’t really work like people do, as much as people would like to claim that they’ve created something that works like the human brain.
Re:Adversarial Examples That Fool Both Computer Vision and T...
by red75prime on Monday November 12, 2018 @10:06AM
(#18432989)
For me people work unnervingly like neural networks, except for cases where stimuli have gone through slow sequential processing by the entirety of brain computational resources, we call consciousness.
Cached responses, for example. A question in a text form, which includes a clause pointing at a flaw in usual answer, gets the same usual answer.
Effectiveness of pointing and calling technique [0], which forces full conscious attention.
And other results in psychology, which can be explained by artificial neural network level dumbness of some behaviors. (If 7 workers can build 7 cars in 7 days, then how many days would it take 5 workers to build 5 cars? Is Winnie-the-Pooh a boar or a pig?)
Re:Adversarial Examples That Fool Both Computer Vision and T...
by aetherson on Monday November 12, 2018 @10:00AM
(#18432953)
Sure. So, then, why did you posit a direct parallel between optical illusions and adversarial images?
Re:Adversarial Examples That Fool Both Computer Vision and T...
by saagarjha on Monday November 12, 2018 @05:59PM
(#18436786)
Because that’s another thing I can now mention when people tell me neural networks are like the human brain, since this is an example they often like to bring up.
Re:Adversarial Examples That Fool Both Computer Vision and T...
by taeric on Monday November 12, 2018 @01:11PM
(#18434353)
That said, one can stop before they get to optical illusions. Simple camouflage works as a similar trick. Consider stick bugs. Unless you see them move, or are directly looking at the facial features, I expect even machines will have a hard time detecting them.
Humans can obviously be tricked, in a variety of ways. But adversarial images take advantage of the fact that image-recognizing neural networks do not fit their image recognition into a full fledged understanding of the world like we do. So a few pixels here and there can make a truck look like a panda and the algorithm never says, "But wait, pandas are mostly black and white and this is mostly yellow," or, "But I don't see legs anywhere, or ears."
Optical illusions mostly don't cause high level image misclassifications. To the extent that they are anything similar, they're the reverse: using our general world understanding to cause glitches in our information processing, such as cases where you think something is darker or lighter than it is, or bigger or smaller, or bent or straight. Those are your mind applying rules that are based on "how the world usually appears at a high level" to an image where those rules do not in fact apply.