基于深度学习的对抗攻击发展研究
Research on the development of adversarial attacks based on deep learning
卢彦利 1石雪莹 1刘光晓 1柳雪飞 1文小慧 2李章敏 3蒋正锋4
作者信息
- 1. 广西民族师范学院数理与电子信息工程学院,崇左 532200
- 2. 江西财经大学统计与数据科学学院,南昌 330013
- 3. 威宁县岔河镇新发小学,毕节 553105
- 4. 广西民族师范学院数理与电子信息工程学院,崇左 532200;武汉大学计算机学院,武汉 430072
- 折叠
摘要
随着深度学习在各领域的广泛应用,对抗攻击问题引起学术界与工业界的关注.首先概述了对抗攻击的背景,包括对抗攻击的定义、分类以及与传统的机器学习安全问题的区别.然后讨论了对抗样本生成及攻击策略,以及白盒攻击和黑盒攻击等攻击手段.最后总结了对抗攻击的意义,并展望未来研究方向,期待通过研究和探索提高深度学习模型的安全性和可靠性.
Abstract
With the widespread application of deep learning in various fields,the issue of adversarial attacks has attracted at-tention from both academia and industry.Firstly,the background of adversarial attacks is outlined,including the definition,classifi-cation,and differences from traditional machine learning security issues.Then we discussed adversarial sample generation and at-tack strategies,as well as attack methods such as white box and black box attacks.Finally,the significance of adversarial attacks was summarized,and future research directions were looked forward to improving the security and reliability of deep learning mod-els through research and exploration.
关键词
深度学习/对抗攻击/数据攻击/模型攻击/防御策略Key words
deep learning/adversarial attacks/data attacks/model attacks/defense strategies引用本文复制引用
基金项目
国家级大学生创新创业训练计划(202210604038)
出版年
2024
NETL
NSTL
万方数据