  Adversarial examples (AEs) are images that can mislead deep neural network
(DNN) classifiers via introducing slight perturbations into original images.
Recent work has shown that detecting AEs can be more effective against AEs than
preventing them from being generated. However, the state-of-the-art AE
detection still shows a high false positive rate, thereby rejecting a
considerable amount of normal images. To address this issue, we propose
HAWKEYE, which is a separate neural network that analyzes the output layer of
the DNN, and detects AEs. HAWKEYE's AE detector utilizes a quantized version of
an input image as a reference, and is trained to distinguish the variation
characteristics of the DNN output on an input image from the DNN output on its
reference image. We also show that cascading our AE detectors that are trained
for different quantization step sizes can drastically reduce a false positive
rate, while keeping a detection rate high.
