深度学习中的对抗攻击与防御

被引:22
作者
刘西蒙 [1 ,2 ]
谢乐辉 [1 ]
王耀鹏 [1 ]
李旭如 [3 ]
机构
[1] 福州大学数学与计算机科学学院
[2] 广东省数据安全与隐私保护重点实验室
[3] 华东师范大学计算机与科学学院
关键词
对抗样本; 对抗攻击; 对抗防御; 深度学习安全;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
对抗样本是被添加微小扰动的原始样本,用于误导深度学习模型的输出决策,严重威胁到系统的可用性,给系统带来极大的安全隐患。为此,详细分析了当前经典的对抗攻击手段,主要包括白盒攻击和黑盒攻击。根据对抗攻击和防御的发展现状,阐述了近年来国内外的相关防御策略,包括输入预处理、提高模型鲁棒性、恶意检测。最后,给出了未来对抗攻击与防御领域的研究方向。
引用
收藏
页码:36 / 53
页数:18
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