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

Despite the impressive performance of Artificial Intelligence (AI) systems, their robustness
remains elusive and constitutes a key issue that impedes large-scale adoption. Besides …

Pretrained language models (PLMs) perform poorly under adversarial attacks. To improve
the adversarial robustness, adversarial data augmentation (ADA) has been widely adopted …

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

Transformer-based models have demonstrated state-of-the-art performance in many
intelligent coding tasks such as code comment generation and code completion. Previous …

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 (DNNs) have achieved remarkable success in various tasks (eg,
image classification, speech recognition, and natural language processing (NLP)). However …

Very recently, few certified defense methods have been developed to provably guarantee
the robustness of a text classifier to adversarial synonym substitutions. However, all the …

Adversarial ranking attacks have gained increasing attention due to their success in probing
vulnerabilities, and, hence, enhancing the robustness, of neural ranking models …

Data-hungry deep neural networks have established themselves as the de facto standard for
many NLP tasks, including the traditional sequence tagging ones. Despite their state-of-the …