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 …

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 …

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

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

Abstract Language models are known to be vulnerable to textual adversarial attacks, which
add humanimperceptible perturbations to the input to mislead DNNs. It is thus imperative to …

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

Despite neural networks have achieved prominent performance on many natural language
processing (NLP) tasks, they are vulnerable to adversarial examples. In this paper, we …

State-of-the-art attacks on NLP models lack a shared definition of a what constitutes a
successful attack. We distill ideas from past work into a unified framework: a successful …

Adversarial examples greatly compromise the security of deep learning models. The key to
improving the robustness of a natural language processing (NLP) model is to study attacks …