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A Minimax Approach to Perturbation Attacks on Hierarchical Image Classifiers

Adversarial attacks on classification models were shown to drastically degrade the performance of simple one- stage classifiers. In this paper, we investigate the vulnerability of Hierarchical Classifiers to such attacks. We formulate a maximin problem to generate perturbations targeting these models. An algorithmic approach is proposed to alter the predicted super class - for instance, changing the prediction of a dog to a car rather than a cat, so that an ‘animal’ is classified as a ‘vehicle’. With the proposed algorithm, the relative loss in classification accuracy for super labels decreases significantly in comparison to perturbations generated for simple classifiers. We also examine the amount of perturbation needed for each case.