19 citations · 35 across the 16 of their papers we have counts for
4 papers · 1 filter
Large Language Models can be Guided to Evade AI-Generated Text Detection
Ning Lu, Shengcai Liu, Rui He +3
Large language models (LLMs) have shown remarkable performance in various tasks and have been extensively utilized by the public. However, the increasing concerns regarding the mis…
Less is More: Understanding Word-level Textual Adversarial Attack via n-gram Frequency Descend
Ning Lu, Shengcai Liu, Zhirui Zhang +3
Word-level textual adversarial attacks have demonstrated notable efficacy in misleading Natural Language Processing (NLP) models. Despite their success, the underlying reasons for…
Effective and Imperceptible Adversarial Textual Attack via Multi-objectivization
Shengcai Liu, Ning Lu, Wenjing Hong +2
The field of adversarial textual attack has significantly grown over the last few years, where the commonly considered objective is to craft adversarial examples (AEs) that can suc…
Efficient Combinatorial Optimization for Word-level Adversarial Textual Attack
Shengcai Liu, Ning Lu, Cheng Chen +1
Over the past few years, various word-level textual attack approaches have been proposed to reveal the vulnerability of deep neural networks used in natural language processing. Ty…