  Although deep neural networks (DNNs) have shown impressive performance on
many perceptual tasks, they are vulnerable to adversarial examples that are
generated by adding slight but maliciously crafted perturbations to benign
images. Adversarial detection is an important technique for identifying
adversarial examples before they are entered into target DNNs. Previous studies
to detect adversarial examples either targeted specific attacks or required
expensive computation. How design a lightweight unsupervised detector is still
a challenging problem. In this paper, we propose an AutoEncoder-based
Adversarial Examples (AEAE) detector, that can guard DNN models by detecting
adversarial examples with low computation in an unsupervised manner. The AEAE
includes only a shallow autoencoder but plays two roles. First, a well-trained
autoencoder has learned the manifold of benign examples. This autoencoder can
produce a large reconstruction error for adversarial images with large
perturbations, so we can detect significantly perturbed adversarial examples
based on the reconstruction error. Second, the autoencoder can filter out the
small noise and change the DNN's prediction on adversarial examples with small
perturbations. It helps to detect slightly perturbed adversarial examples based
on the prediction distance. To cover these two cases, we utilize the
reconstruction error and prediction distance from benign images to construct a
two-tuple feature set and train an adversarial detector using the isolation
forest algorithm. We show empirically that the AEAE is unsupervised and
inexpensive against the most state-of-the-art attacks. Through the detection in
these two cases, there is nowhere to hide adversarial examples.
