Rodrigo Agundez shows how to fool neural networks:
A comprehensive and complete summary can be found in the When DNNs go wrong blog, which I recommend you to read.
All these amazing studies use state of the art deep learning techniques, which makes them (in my opinion) difficult to reproduce and to answer questions we might have as non-experts in this subject.
My intention in this blog is to bring the main concepts down to earth, to an easily reproducible setting where they are clear and actually visible. In addition, I hope this short blog can provide a better understanding of the limitations of discriminative models in general. The complete code used in this blog post can be found here.
This is a great article.