  Security-sensitive applications that rely on Deep Neural Networks (DNNs) are
vulnerable to small perturbations that are crafted to generate Adversarial
Examples(AEs). The AEs are imperceptible to humans and cause DNN to misclassify
them. Many defense and detection techniques have been proposed. Model's
confidences and Dropout, as a popular way to estimate the model's uncertainty,
have been used for AE detection but they showed limited success against black-
and gray-box attacks. Moreover, the state-of-the-art detection techniques have
been designed for specific attacks or broken by others, need knowledge about
the attacks, are not consistent, increase model parameters overhead, are
time-consuming, or have latency in inference time. To trade off these factors,
we revisit the model's uncertainty and confidences and propose a novel
unsupervised ensemble AE detection mechanism that 1) uses the uncertainty
method called SelectiveNet, 2) processes model layers outputs, i.e.feature
maps, to generate new confidence probabilities. The detection method is called
Selective and Feature based Adversarial Detection (SFAD). Experimental results
show that the proposed approach achieves better performance against black- and
gray-box attacks than the state-of-the-art methods and achieves comparable
performance against white-box attacks. Moreover, results show that SFAD is
fully robust against High Confidence Attacks (HCAs) for MNIST and partially
robust for CIFAR10 datasets.
