Adversarial noise is interesting, but I think it's more of a toy problem that will go away as we switch to more sophisticated models with life-long learning tha

Adversarial noise is interesting, but I think it's more of a toy problem that will go away as we switch to more sophisticated models with life-long learning that are able to develop complicated, hierarchical/compositional classifications. For instance, there was a talk at NIPS this year where the researcher developed virtual stickers that can be added to a photo and cause the image to be misclassified (eg you have a "banana" sticker that you can apply to any picture to make the classifier classify that picture as a banana). The underlying issue, IMO, is that the classifier has only a limited number of categories from which to choose, and can't develop new categories (such as "adversarially perturbed picture"). The modified image does look more like a banana than anything else, and as the classifier never trained on adversarial images as a separate category it doesn't really make sense to make any other classifications.