A Method for Computing Class-wise Universal Adversarial Perturbations

Authors: Tejus Gupta, Abhishek Sinha, Nupur Kumari, Mayank Singh, Balaji Krishnamurthy
Published: 2019-12-01

Source: https://arxiv.org/abs/1912.00466

PDF: https://arxiv.org/pdf/1912.00466

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敵対的サンプルの脆弱性 敵対的サンプル 深層学習

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Abstract

We present an algorithm for computing class-specific universal adversarial perturbations for deep neural networks. Such perturbations can induce misclassification in a large fraction of images of a specific class. Unlike previous methods that use iterative optimization for computing a universal perturbation, the proposed method employs a perturbation that is a linear function of weights of the neural network and hence can be computed much faster. The method does not require any training data and has no hyper-parameters. The attack obtains 34 state-of-the-art deep neural networks on ImageNet and transfers across models. We also study the characteristics of the decision boundaries learned by standard and adversarially trained models to understand the universal adversarial perturbations.