Adversarial training based on the minimax formulation is necessary for obtaining adversarial
robustness of trained models. However, it is conservative or even pessimistic so that it …

Adversarial training has been recognized as a strong defense against adversarial attacks. In
this paper, we propose a powerful second-order attack method that reduces the accuracy of …

Adversarial training is one of the most effective approaches to improve model robustness
against adversarial examples. However, previous works mainly focus on the overall …

Deep neural networks (DNNs) are vulnerable to adversarial examples crafted by
imperceptible perturbations. A range of defense techniques have been proposed to improve …

Abstract Deep Neural Networks (DNN) have been shown to be vulnerable to adversarial
examples. Adversarial training (AT) is a popular and effective strategy to defend against …

Recent work has uncovered the interesting (and somewhat surprising) finding that training
models to be invariant to adversarial perturbations requires substantially larger datasets …

Although fast adversarial training has demonstrated both robustness and efficiency, the
problem of" catastrophic overfitting" has been observed. This is a phenomenon in which …

Adversarial training algorithms have been proved to be reliable to improve machine learning
models' robustness against adversarial examples. However, we find that adversarial training …

Many machine learning models are vulnerable to adversarial attacks; for example, adding
adversarial perturbations that are imperceptible to humans can often make machine …

Current neural-network-based classifiers are susceptible to adversarial examples. The most
empirically successful approach to defending against such adversarial examples is …