Great advances in deep neural networks (DNNs) have led to state-of-the-art performance on
a wide range of tasks. However, recent studies have shown that DNNs are vulnerable to …

Transferable adversarial attacks against Deep neural networks (DNNs) have received broad
attention in recent years. An adversarial example can be crafted by a surrogate model and …

Recent studies show that training deep neural networks (DNNs) with Lipschitz constraints
are able to enhance adversarial robustness and other model properties such as stability. In …

It is widely recognized that deep learning models lack robustness to adversarial examples.
An intriguing property of adversarial examples is that they can transfer across different …

Recent studies show that deep neural networks (DNN) are vulnerable to adversarial
examples, which aim to mislead DNNs by adding perturbations with small magnitude. To …

The score-based query attacks (SQAs) pose practical threats to deep neural networks by
crafting adversarial perturbations within dozens of queries, only using the model's output …

Adversarial examples for neural network image classifiers are known to be transferable:
examples optimized to be misclassified by a source classifier are often misclassified as well …

As data becomes increasingly vital, a company would be very cautious about releasing data,
because the competitors could use it to train high-performance models, thereby posing a …

Adversarial examples for deep neural networks (DNNs) are transferable: examples that
successfully fool one white-box surrogate model can also deceive other black-box models …

The strategy of ensemble has become popular in adversarial defense, which trains multiple
base classifiers to defend against adversarial attacks in a cooperative manner. Despite the …