With the rising popularity of deep neural networks in real applications, it is important to understand when and how neural networks might behave in undesirable ways. This paper highlighted the issue that neural networks can be vulnerable to small almost imperceptible variations to the input. This idea helped spawn the area of adversarial attacks (trying to fool a neural network) as well as adversarial defense (training a neural network to not be fooled).