The adversarial attacks against deep neural networks on computer vision tasks have
spawned many new technologies that help protect models from avoiding false predictions …

Recent studies have shown that deep neural networks are vulnerable to intentionally crafted
adversarial examples, and various methods have been proposed to defend against …

Up until very recently, inspired by a mass of researches on adversarial examples for
computer vision, there has been a growing interest in designing adversarial attacks for …

Deep Neural Networks have taken Natural Language Processing by storm. While this led to
incredible improvements across many tasks, it also initiated a new research field …

In the area of natural language processing, deep learning models are recently known to be
vulnerable to various types of adversarial perturbations, but relatively few works are done on …

Although deep neural networks have achieved prominent performance on many NLP tasks,
they are vulnerable to adversarial examples. We propose Dirichlet Neighborhood Ensemble …

Deep neural networks are known to be vulnerable to various types of adversarial attacks,
especially word-level attacks, in the field of natural language processing. In recent years …

Adversarial attacks on deep neural networks keep raising security concerns in natural
language processing research. Existing defenses focus on improving the robustness of the …

Deep neural networks (DNNs) are vulnerable to adversarial attacks, in which a small
perturbation to samples can cause misclassification. However, how to select important …

Adversarial attacks are carried out to reveal the vulnerability of deep neural networks.
Textual adversarial attacking is challenging because text is discrete and a small perturbation …