  Deep Neural Networks (DNNs) are vulnerable to adversarial examples, while
adversarial attack models, e.g., DeepFool, are on the rise and outrunning
adversarial example detection techniques. This paper presents a new adversarial
example detector that outperforms state-of-the-art detectors in identifying the
latest adversarial attacks on image datasets. Specifically, we propose to use
sentiment analysis for adversarial example detection, qualified by the
progressively manifesting impact of an adversarial perturbation on the
hidden-layer feature maps of a DNN under attack. Accordingly, we design a
modularized embedding layer with the minimum learnable parameters to embed the
hidden-layer feature maps into word vectors and assemble sentences ready for
sentiment analysis. Extensive experiments demonstrate that the new detector
consistently surpasses the state-of-the-art detection algorithms in detecting
the latest attacks launched against ResNet and Inception neutral networks on
the CIFAR-10, CIFAR-100 and SVHN datasets. The detector only has about 2
million parameters, and takes shorter than 4.6 milliseconds to detect an
adversarial example generated by the latest attack models using a Tesla K80 GPU
card.
