The Current Status and progress of Adversarial Examples Attacks
Chaoran Yuan, Xiaobin Liu, Zhengyuan Zhang
Abstract
Applications based on deep learning are prevailing in many real-world scenarios. However, due to the interpretability of deep learning and the emergence of adversarial examples attacks, their reliability has been put into doubt. As for Natural Language Processing, adversarial samples that can be semantically similar to the original ones can also be generated so that NLP classification can be fooled without human observers' attention. In this paper, we give an overview of the existing method in implementing adversarial examples attacks. Firstly, we introduce the implementation of these methods and the damage they impose on the security, integrity, and robustness of NLP systems. Secondly, we discuss problems like the limited attention on the logic of defense, the curse of dimensionality, and the difficulty of implementing white-box adversaries. Finally, to present our opinion on ML security's structure, our discussion upon the standardization of both attack and defense is also included.