Deep Learning (DL) has shown great success in many human-related tasks, which
has led to its adoption in many computer vision based applications, such as
security surveillance system, autonomous vehicles and healthcare. Such
safety-critical applications have to draw its path to success deployment once
they have the capability to overcome safety-critical challenges. Among these
challenges are the defense against or/and the detection of the adversarial
example (AE). Adversary can carefully craft small, often imperceptible, noise
called perturbations, to be added to the clean image to generate the AE. The
aim of AE is to fool the DL model which makes it a potential risk for DL
applications. Many test-time evasion attacks and countermeasures, i.e., defense
or detection methods, are proposed in the literature. Moreover, few reviews and
surveys were published and theoretically showed the taxonomy of the threats and
the countermeasure methods with little focus in AE detection methods. In this
paper, we attempt to provide a theoretical and experimental review for AE
detection methods. A detailed discussion for such methods is provided and
experimental results for eight state-of-the-art detectors are presented under
different scenarios on four datasets. We also provide potential challenges and
future perspectives for this research direction.

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