In the past few years, it has become increasingly evident that deep neural networks are not
resilient enough to withstand adversarial perturbations in input data, leaving them …

With the development of high computational devices, deep neural networks (DNNs), in
recent years, have gained significant popularity in many Artificial Intelligence (AI) …

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

Deep learning models have achieved great success in solving a variety of natural language
processing (NLP) problems. An ever-growing body of research, however, illustrates the …

Adversarial attacks are a major challenge faced by current machine learning research.
These purposely crafted inputs fool even the most advanced models, precluding their …

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

Deep neural networks (DNN) have achieved unprecedented success in numerous machine
learning tasks in various domains. However, the existence of adversarial examples raises …

Adversarial training, a method for learning robust deep neural networks, constructs
adversarial examples during training. However, recent methods for generating NLP …

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 (DNNs) are vulnerable to adversarial noise. Their adversarial
robustness can be improved by exploiting adversarial examples. However, given the …