A Comparative Analysis of White Box and Gray Box Adversarial Attacks to Natural Language Processing Systems

DOI

10.2991/978-94-6463-540-9_65How to use a DOI?

Keywords

Natural Language Processing; Deep Learning; White Box Attack; Gray Box Attack

Abstract

This article comprehensively describes natural language processing (NLP) and its relationship to adversarial attacks. As an interdisciplinary field involving computer science, artificial intelligence, and linguistics, the NLP has great potential to transform all walks of life. Deep learning, as the main technology of NLP, achieves great advancement in tasks such as machine translation, image recognition, and speech understanding, but also faces challenges such as feature optimization and generalization problems. The emergence of adversarial attacks has attracted attention, especially white and grey box attack techniques. Among these approaches, white box attack refers to the attack initiated when the attacker fully understands the model, while the gray box attack is closer to the reality, and the attacker has some knowledge. This paper introduces some typical gray box attack methods, such as model theft, migration-based attack and limited information attack, highlighting the importance of defense mechanism. Interdisciplinary collaboration is necessary to promote collaboration among computer science, cybersecurity, and linguistics researchers to develop comprehensive solutions. Future research should prioritize the development of adaptive defense mechanisms and enhance the transparency and accountability of the NLP models to protect the integrity and credibility of the system.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Hua Feng
AU  - Shangyi Li
AU  - Haoyuan Shi
AU  - Zhixun Ye
PY  - 2024
DA  - 2024/10/16
TI  - A Comparative Analysis of White Box and Gray Box Adversarial Attacks to Natural Language Processing Systems
BT  - Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024)
PB  - Atlantis Press
SP  - 640
EP  - 646
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-540-9_65
DO  - 10.2991/978-94-6463-540-9_65
ID  - Feng2024
ER  -