  Detecting adversarial examples currently stands as one of the biggest
challenges in the field of deep learning. Adversarial attacks, which produce
adversarial examples, increase the prediction likelihood of a target class for
a particular data point. During this process, the adversarial example can be
further optimized, even when it has already been wrongly classified with 100%
confidence, thus making the adversarial example even more difficult to detect.
For this kind of adversarial examples, which we refer to as over-optimized
adversarial examples, we discovered that the logits of the model provide solid
clues on whether the data point at hand is adversarial or genuine. In this
context, we first discuss the masking effect of the softmax function for the
prediction made and explain why the logits of the model are more useful in
detecting over-optimized adversarial examples. To identify this type of
adversarial examples in practice, we propose a non-parametric and
computationally efficient method which relies on interquartile range, with this
method becoming more effective as the image resolution increases. We support
our observations throughout the paper with detailed experiments for different
datasets (MNIST, CIFAR-10, and ImageNet) and several architectures.
