Deep neural networks (DNNs) have progressed rapidly during the past decade and have
been deployed in various real-world applications. Meanwhile, DNN models have been …

The recent advancements in large language models (LLMs) have sparked a growing
apprehension regarding the potential misuse. One approach to mitigating this risk is to …

The advent of the Internet of Things (IoT) has brought forth an era of unprecedented
connectivity, with an estimated 80 billion smart devices expected to be in operation by the …

Recent years have seen a proliferation of research on adversarial machine learning.
Numerous papers demonstrate powerful algorithmic attacks against a wide variety of …

Evasion attacks are a threat to machine learning models, where adversaries attempt to affect
classifiers by injecting malicious samples. An alarming side-effect of evasion attacks is their …

With the wide applications of deep neural networks (DNNs) in various fields, current
research shows their serious security risks due to the lack of privacy protection. Observing …

Watermarking is a technique used to embed a hidden signal in the probability distribution of
text generated by large language models (LLMs), enabling attribution of the text to the …

Abstract Natural Language Processing (NLP) has revolutionized industries, streamlining
customer service through applications in healthcare, finance, legal, and human resources …

Adversarial samples mostly aim at fooling machine learning (ML) models. They often involve
minor pixel-based perturbations that are imperceptible to human observers. In this work …

Machine learning (ML) models, demonstrably powerful, suffer from a lack of interpretability.
The absence of transparency, often referred to as the black box nature of ML models …