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Deep computer vision systems being vulnerable to imperceptible and carefullycrafted noise have raised questions regarding the robustness of theirdecisions. We take a step back and approach this problem from an orthogonaldirection. We propose to enable black-box neural networks to justify theirreasoning both for clean and for adversarial examples by leveraging attributes,i.e. visually discriminative properties of objects. We rank attributes based ontheir class relevance, i.e. how the classification decision changes when theinput is visually slightly perturbed, as well as image relevance, i.e. how wellthe attributes can be localized on both clean and perturbed images. We presentcomprehensive experiments for attribute prediction, adversarial examplegeneration, adversarially robust learning, and their qualitative andquantitative analysis using predicted attributes on three benchmark datasets.

Interpreting Adversarial Examples with Attributes