Adversarial Example Detection for DNN Models: A Review and Experimental Comparison. (arXiv:2105.00203v4 [cs.CV] UPDATED)

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 systems, autonomous vehicles and healthcare. Such
safety-critical applications have to draw their 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
examples (AEs). Adversaries can carefully craft small, often imperceptible,
noise called perturbations to be added …

arxiv comparison cv detection experimental review