GreedyFool: Multi-Factor Imperceptibility and Its Application to Designing a Black-box Adversarial Attack
This work addresses the challenge of creating more stealthy adversarial attacks for deep neural networks, which is an incremental improvement in adversarial machine learning.
The paper tackled the problem of designing imperceptible adversarial examples by proposing a multi-factor metric (MulFactorLoss) based on human visual system factors, and introduced GreedyFool, a black-box attack that achieved a 100% success rate on ImageNet and CIFAR-10 datasets.
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.