功能木马:通过功能中毒和微调注射来加强隐蔽和稳定的后门攻击
Rui Yang1, Qindong Sun1, Han Cao2
1School of Cyber Science and Engineering, Xi'an Jiaotong University, Xi'an, 710049, China.
概括
特点Trojan为深度神经网络 (DNN) 引入了一种新的后门攻击,将不可察觉的触发器嵌入到潜在空间特征中,以增强隐形性和稳定性. 这种方法显著提高了针对DNN后门防御的攻击成功率.
科学领域:
- 人工智能的人工智能
- 机器学习安全 机器学习安全
- 深度神经网络 深度神经网络
背景情况:
- 深度神经网络 (DNN) 容易受到后门攻击,从而损害其完整性.
- 现有的后门攻击通常使用可检测的触发器,并表现出不稳定的行为.
- 这些局限性阻碍了DNN在安全关键应用中的有效性.
研究的目的:
- 介绍FeatureTrojan,这是一个新的DNN的后门攻击.
- 为了提高DNN的后门攻击的隐形性和稳定性.
- 克服当前后门攻击方法的局限性.
主要方法:
- 特征特洛伊木马嵌入动态,人类不可察觉的触发器在干净输入的潜在空间特征.
- 它利用预训练的扩散生成模型,用这些触发器创建有毒的输入.
- 该方法微调预先训练的DNN,注入后门,同时保持参数接近清洁模型.
主要成果:
- 特征:特洛伊木马在对DNN的后门攻击中显著提高了隐形性和稳定性.
- 实验显示,攻击成功率 (ASR) 对各种防御的平均改善约为30.0%.
- 该攻击在多个数据集和DNN架构中被证明是有效的.
结论:
- 特征 特洛伊木马代表了DNN后门攻击技术的重大进步.
- 与现有攻击相比,拟议的方法提供了一种更强大和更隐蔽的方法.
- 特征:特洛伊木马的有效性凸显了对先进后门威胁更复杂的防御机制的需求.
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