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

Over the last few years, the adoption of machine learning in a wide range of domains has
been remarkable. Deep learning, in particular, has been extensively used to drive …

Recent advances in large language models (LLMs) and the intensifying popularity of
ChatGPT-like applications have blurred the boundary of high-quality text generation …

Adversarial attacks for discrete data (such as texts) have been proved significantly more
challenging than continuous data (such as images) since it is difficult to generate adversarial …

In the past few decades, artificial intelligence (AI) technology has experienced swift
developments, changing everyone's daily life and profoundly altering the course of human …

In recent years, machine learning algorithms, and more specifically deep learning
algorithms, have been widely used in many fields, including cyber security. However …

As NLP models achieved state-of-the-art performances over benchmarks and gained wide
applications, it has been increasingly important to ensure the safe deployment of these …

Abstract Leave-One-Out (LOO) scores provide estimates of feature importance in neural
networks, for adversarial attacks. In this work, we present context-free word scores as a …

Machine learning (ML) provides effective means to learn from spectrum data and solve
complex tasks involved in wireless communications. Supported by recent advances in …

Recent efforts have shown that neural text processing models are vulnerable to adversarial
examples, but the nature of these examples is poorly understood. In this work, we show that …