SSCAE -- Semantic, Syntactic, and Context-aware natural language Adversarial Examples generator

arXiv:2403.11833v1 Announce Type: cross
Abstract: Machine learning models are vulnerable to maliciously crafted Adversarial Examples (AEs). Training a machine learning model with AEs improves its robustness and stability against adversarial attacks. It is essential to develop models that produce high-quality AEs. Developing such models has been much slower in natural language processing (NLP) than in areas such as computer vision. This paper introduces a practical and efficient adversarial attack model called SSCAE for \textbf{S}emantic, \textbf{S}yntactic, and \textbf{C}ontext-aware natural language \textbf{AE}s …

abstract adversarial adversarial attacks adversarial examples arxiv attacks context cs.cl cs.cr cs.lg examples generator language machine machine learning machine learning model machine learning models natural natural language quality robustness semantic stability training type vulnerable