Adversarial examples expose the vulnerabilities of natural language processing (NLP)
models, and can be used to evaluate and improve their robustness. Existing techniques of …

Despite recent success on various tasks, deep learning techniques still perform poorly on
adversarial examples with small perturbations. While optimization-based methods for …

Efficiently building an adversarial attacker for natural language processing (NLP) tasks is a
real challenge. Firstly, as the sentence space is discrete, it is difficult to make small …

Textual adversarial attacking has received wide and increasing attention in recent years.
Various attack models have been proposed, which are enormously distinct and …

Recently, the textual adversarial attack models become increasingly popular due to their
successful in estimating the robustness of NLP models. However, existing works have …

Abstract Machine learning algorithms are often vulnerable to adversarial examples that have
imperceptible alterations from the original counterparts but can fool the state-of-the-art …

We study an important and challenging task of attacking natural language processing
models in a hard label black box setting. We propose a decision-based attack strategy that …

Large-scale pre-trained language models have achieved tremendous success across a
wide range of natural language understanding (NLU) tasks, even surpassing human …

Recently, the adversarial attack and defense technology has made remarkable
achievements and has been widely applied in the computer vision field, promoting its rapid …

Adversarial attacks against natural language processing systems, which perform seemingly
innocuous modifications to inputs, can induce arbitrary mistakes to the target models …