  By adding carefully crafted perturbations to input images, adversarial
examples (AEs) can be generated to mislead neural-network-based image
classifiers. $L_2$ adversarial perturbations by Carlini and Wagner (CW) are
among the most effective but difficult-to-detect attacks. While many
countermeasures against AEs have been proposed, detection of adaptive CW-$L_2$
AEs is still an open question. We find that, by randomly erasing some pixels in
an $L_2$ AE and then restoring it with an inpainting technique, the AE, before
and after the steps, tends to have different classification results, while a
benign sample does not show this symptom. We thus propose a novel AE detection
technique, Erase-and-Restore (E&R), that exploits the intriguing sensitivity of
$L_2$ attacks. Experiments conducted on two popular image datasets, CIFAR-10
and ImageNet, show that the proposed technique is able to detect over 98% of
$L_2$ AEs and has a very low false positive rate on benign images. The
detection technique exhibits high transferability: a detection system trained
using CW-$L_2$ AEs can accurately detect AEs generated using another $L_2$
attack method. More importantly, our approach demonstrates strong resilience to
adaptive $L_2$ attacks, filling a critical gap in AE detection. Finally, we
interpret the detection technique through both visualization and
quantification.
