与NLP模型相比,具有高查询效率的硬标签对抗性攻击
Shilin Qiu1, Qihe Liu2, Shijie Zhou2
1School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China. shilinqiu@std.uestc.edu.cn.
Scientific reports
|March 19, 2025
概括
本研究介绍了QEAttack,这是一种针对自然语言处理模型生成对抗性文本的新方法. QEAttack显著减少了查询数量,提高了对抗性攻击的效率.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 计算机安全 计算机安全
背景情况:
- 黑盒子对抗性攻击有效地揭示了自然语言处理 (NLP) 模型的漏洞.
- 由于查询数量很高,目前的攻击方法效率低下,限制了实际应用.
研究的目的:
- 提出QEAttack,一个用于NLP模型的查询效率高的硬标攻击方法.
- 用最少的查询生成具有说服力和语义等价的对抗性文本.
主要方法:
- 利用遗传算法进行对抗性文本生成.
- 采用双梯度融合策略,减少交叉期间的查询数量.
- 使用局部敏感的散列用于句子级语义聚类在突变中.
主要成果:
- 在各种NLP模型和数据集中,QEAttack实现了高攻击成功率.
- 与现有方法相比,显著减少了查询数量.
- 保持或提高对抗性文本的不可察觉性和质量.
结论:
- 对于硬标签黑子对抗性攻击,QEAttack提供了一种高效有效的解决方案.
- 提出的策略成功地解决了当前对抗性文本生成技术的查询效率低下问题.
- 在NLP模型稳定性评估中,QEAttack提高了对抗性攻击的实际适用性.
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