  Adversarial examples (AEs) are images that can mislead deep neural network
(DNN) classifiers via introducing slight perturbations into original images.
This security vulnerability has led to vast research in recent years because it
can introduce real-world threats into systems that rely on neural networks.
Yet, a deep understanding of the characteristics of adversarial examples has
remained elusive. We propose a new way of achieving such understanding through
a recent development, namely, invertible neural models with Lipschitz
continuous mapping functions from the input to the output. With the ability to
invert any latent representation back to its corresponding input image, we can
investigate adversarial examples at a deeper level and disentangle the
adversarial example's latent representation. Given this new perspective, we
propose a fast latent space adversarial example generation method that could
accelerate adversarial training. Moreover, this new perspective could
contribute to new ways of adversarial example detection.
