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Robustness May Be at Odds with Fairness: An Empirical Study on Class-wise Accuracy

Recently, convolutional neural networks (CNNs) have made significant advancement, however, they are widely known to be vulnerable to adversarial attacks. Adversarial training is the most widely used technique for improving adversarial robustness to …

Batch Normalization Increases Adversarial Vulnerability: Disentangling Usefulness and Robustness of Model Features

Batch normalization (BN) has been widely used in modern deep neural networks (DNNs) due to fast convergence. BN is observed to increase the model accuracy while at the cost of adversarial robustness. We conjecture that the increased adversarial …