HyGloadAttack:通过混合优化进行硬标签黑盒文本对抗性攻击.
Zhaorong Liu1, Xi Xiong1, Yuanyuan Li2
1School of Cybersecurity, Chengdu University of Information Technology, Chengdu 610225, China; Advanced Cryptography and System Security Key Laboratory of Sichuan Province, Chengdu 610225, China; SUGON Industrial Control and Security Center, Chengdu 610225, China.
概括
本研究介绍了HyGloadAttack,这是一种创建强大的对抗性文本示例的新方法. 它提高了语义相似性,减少了干扰,在对抗性攻击中表现优于现有的技术.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习安全 机器学习安全
- 敌对的机器学习
背景情况:
- 由于文本的离散性和有限的模型访问,硬标签黑子文本对抗性攻击具有挑战性.
- 现有的方法,如基于交换和基于梯度的攻击,受到局部优化和高查询要求的影响.
- 在有限的查询下生成具有语义相似性和低扰动性的高质量对抗示例仍然是一个开放的问题.
研究的目的:
- 提出一个新的框架,HyGloadAttack,用于生成高质量的对抗性文本示例.
- 解决现有方法在文字对抗性攻击的性能和效率方面的局限性.
- 为了增强语义相似性,并在受约束查询条件下的对抗性示例中最大限度地减少干扰.
主要方法:
- HyGloadAttack使用一个扰动矩阵在词嵌入空间全球初始化.
- 它选择同义词以最大限度地增加相似性,同时保持对抗性质.
- 基于梯度的快速搜索可以加速优化过程.
主要成果:
- 在最先进的基线方法中,HyGloadAttack显示出明显的优势.
- 对五个数据集进行了实验,用于文本分类和自然语言推断.
- 该方法还在两个真实世界的API上得到了验证.
结论:
- HyGloadAttack有效地生成了高质量的对抗性文本示例.
- 拟议的框架克服了以前在效率和干扰控制方面的方法的局限性.
- 这项工作推进了硬标签黑子文本对抗性攻击的领域.
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