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Most current methods generate adversarial examples with the $L_p$ normspecification. As a result, many defense methods utilize this property toeliminate the impact of such attacking algorithms. In this paper,we insteadintroduce "unrestricted" perturbations that create adversarial samples by usingspurious relations which were learned by model training. Specifically, we findfeature clusters in non-semantic features that are strongly correlated withmodel judgment results, and treat them as spurious relations learned by themodel. Then we create adversarial samples by using them to replace thecorresponding feature clusters in the target image. Experimental evaluationsshow that in both black-box and white-box situations. Our adversarial examplesdo not change the semantics of images, while still being effective at foolingan adversarially trained DNN image classifier.

Unrestricted Adversarial Samples Based on Non-semantic Feature Clusters Substitution