  Adversarial examples are well-designed input samples, in which perturbations
are imperceptible to the human eyes, but easily mislead the output of deep
neural networks (DNNs). Existing works synthesize adversarial examples by
leveraging simple metrics to penalize perturbations, that lack sufficient
consideration of the human visual system (HVS), which produces noticeable
artifacts. To explore why the perturbations are visible, this paper summarizes
four primary factors affecting the perceptibility of human eyes. Based on this
investigation, we design a multi-factor metric MulFactorLoss for measuring the
perceptual loss between benign examples and adversarial ones. In order to test
the imperceptibility of the multi-factor metric, we propose a novel black-box
adversarial attack that is referred to as GreedyFool. GreedyFool applies
differential evolution to evaluate the effects of perturbed pixels on the
confidence of a target DNN, and introduces greedy approximation to
automatically generate adversarial perturbations. We conduct extensive
experiments on the ImageNet and CIFRA-10 datasets and a comprehensive user
study with 60 participants. The experimental results demonstrate that
MulFactorLoss is a more imperceptible metric than the existing pixelwise
metrics, and GreedyFool achieves a 100% success rate in a black-box manner.
