It seems like the work around for that is to fuzz the images slightly before feeding them to the neural net?
'Fuzzing' and other forms of modification (I think the general term is 'data augmentation', and there can be quite a few different ways to modify images to increase your sample size - the paper I discuss in the grandparent spends two pages or so listing all the methods it uses) aren't a fix.
In this case, they say they are using AlexNet which already does some data augmentation (pg5-6).
Further, if you treat the adversarial examples as another data augmentation trick and train the networks with the old examples, you can still generate more adversarial exampl...
If it's worth saying, but not worth its own post (even in Discussion), then it goes here.
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