The human visual system is susceptible to misperception, sure. This has been known for awhile. I also would agree that sometimes single pixels could probably co

The human visual system is susceptible to misperception, sure. This has been known for awhile. I also would agree that sometimes single pixels could probably confuse people substantially. However, if you look at the images in the article, I think it's telling about how much more sophisticated human perception is. The authors even have a note in the first image along the lines of "look carefully, because you might have trouble locating the aberrant pixel." I think these types of attacks speak volumes about the fragility of DL optimizations: I think there's more overfitting going on than people acknowledge or realize. My sense from reading in working with some ML things is that this extends to natural language data as well. DL systems are often highly optimized to a particular test set, and might do well on cross-validation to other exemplars of that test set, but that's not the same as generalizing across different types of test sets. Maybe I'm wrong, though--I think DL NN models have been fantastic, but there's a certain amount of hype in ML in general.