Even before deep learning architectures became the de facto models for
complex computer vision tasks, the softmax function was, given its elegant
properties, already used to analyze the predictions of feedforward neural
networks. Nowadays, the output of the softmax function is also commonly used to
assess the strength of adversarial examples: malicious data points designed to
fail machine learning models during the testing phase. However, in this paper,
we show that it is possible to generate adversarial examples that take
advantage of some properties of the softmax function, leading to undesired
outcomes when interpreting the strength of the adversarial examples at hand.
Specifically, we argue that the output of the softmax function is a poor
indicator when the strength of an adversarial example is analyzed and that this
indicator can be easily tricked by already existing methods for adversarial
example generation