Previous studies have shown that universal adversarial attacks can fool deep neural
networks over a large set of input images with a single human-invisible perturbation …

The previous study has shown that universal adversarial attacks can fool deep neural
networks over a large set of input images with a single human-invisible perturbation …

Despite their impressive performance, deep neural networks (DNNs) are widely known to be
vulnerable to adversarial attacks, which makes it challenging for them to be deployed in …

Deep learning models have shown to be susceptible to universal adversarial perturbation
(UAP), which has aroused wide concerns in the community. Compared with the …

Neural networks are vulnerable to adversarial examples, which are malicious inputs crafted
to fool pre-trained models. Adversarial examples often exhibit black-box attacking …

Strong adversarial examples are crucial for evaluating and enhancing the robustness of
deep neural networks. However the performance of popular attacks is usually sensitive for …

Adversarial examples are perturbed inputs designed to fool machine learning models.
Adversarial training injects such examples into training data to increase robustness. To …

While effective, deep neural networks (DNNs) are vulnerable to adversarial attacks. In
particular, recent work has shown that such attacks could be generated by another deep …

Deep neural networks (DNNs) are shown to be vulnerable to universal adversarial
perturbations (UAP), a single quasi-imperceptible perturbation that deceives the DNNs on …

Adversarial examples contain small perturbations that can remain imperceptible to human
observers but alter the behavior of even the best performing deep learning models and yield …