Adversarial attacks and defenses in natural language processing - Dong, Xinshuai (Nanyang Technological University, 2022)
Deep neural networks (DNNs) are becoming increasingly successful in many fields. However, DNNs are shown to be strikingly susceptible to adversarial examples. For instance, models pre-trained on very large corpora can still be easily fooled by word substitution attacks using only synonyms. This phenomenon has raised grand security challenges to modern machine learning systems, such as self-driving, spam filtering, and speech recognition, where DNNs are widely deployed.