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Really? I believe they meant exactly what they said. Read https://arxiv.org/abs/1605.07277 https://arxiv.org/abs/1605.07277 Adversarial examples can be easily
by shmel 9y ago
Really? I believe they meant exactly what they said. Read https://arxiv.org/abs/1605.07277 https://arxiv.org/abs/1605.07277
Adversarial examples can be easily crafted for linear regression, SVM, decision trees and k-NN at very least. I can't think about any ML technique that is proven to be secure against them.
There is also an old blogpost from Karpathy describing that the only reason we are not concerned about adversarial examples in linear models is that nobody uses them to classify ImageNet.
- j2kun 9y agoYes, many models are vulnerable. But there are no proofs that allow you to say that. You should also be aware that they're only talking about image classification in all of these. I will only accept blanket statements about "all machine learning systems" when there's a mathematical proof. I don't mean to lessen the importance of the work, just to point out that saying "X fails at Y on subtask Z using the techniques we have today" is very different from saying "All possible X's, current and future, fail at Y."