@article {5092,
	title = {PCA as a defense against some adversaries},
	year = {2022},
	abstract = {<p>Neural network classifiers are known to be highly vulnerable to adversarial perturbations in their inputs. Under the hypothesis that adversarial examples lie outside of the sub-manifold of natural images, previous work has investigated the impact of principal components in data on adversarial robustness. In this paper we show that there exists a very simple defense mechanism in the case where adversarial images are separable in a previously defined $(k,p)$ metric. This defense is very successful against the\&nbsp; popular Carlini-Wagner attack, but less so against some other common attacks like FGSM. It is interesting to note that the defense is still successful for relatively large perturbations.</p>
},
	author = {Aparna Gupte and Andrzej Banburski and Tomaso Poggio}
}
