  Security-sensitive applications that relay on Deep Neural Networks (DNNs) are
vulnerable to small perturbations crafted to generate Adversarial Examples
(AEs) that are imperceptible to human and cause DNN to misclassify them. Many
defense and detection techniques have been proposed. 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 propose a novel unsupervised detection mechanism
that uses the selective prediction, processing model layers outputs, and
knowledge transfer concepts in a multi-task learning setting. It is called
Selective and Feature based Adversarial Detection (SFAD). Experimental results
show that the proposed approach achieves comparable results to the
state-of-the-art methods against tested attacks in white box scenario and
better results in black and gray boxes scenarios. Moreover, results show that
SFAD is fully robust against High Confidence Attacks (HCAs) for MNIST and
partially robust for CIFAR-10 datasets.
