几秒钟的后门:通过模型编辑解锁大型预训练模型中的漏洞
Dongliang Guo1, Mengxuan Hu1, Zihan Guan1
1University of Virginia, Charlottesville, Virginia, USA.
概括
研究人员开发了EDT,这是一种高效,无数据和无训练的后门攻击方法,用于大型预训练模型. 这种新的方法解决了攻击复杂的人工智能系统的挑战,而不需要训练数据或模型再训练.
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
- 计算机视觉 计算机视觉
背景情况:
- 大型预训练模型 (例如,ViT) 强大,但易受后门攻击的影响.
- 现有的攻击需要访问训练数据和大量的计算资源,这对大型模型构成挑战.
研究的目的:
- 在大型预训练模型上调查后门攻击的独特挑战.
- 开发一个有效和可行的后门攻击方法,适合这些模型.
主要方法:
- 引入了EDT (高效,无数据,无培训),一种新的后门攻击方法.
- 通过注入轻量级代码本来修改模型行为,而无需数据中毒或再培训,EDT利用模型编辑技术.
主要成果:
- 在各种预训练模型 (ViT,CLIP,BLIP,稳定扩散) 中证明了EDT的有效性.
- 在各种下游任务上验证了攻击的成功,例如图像分类,标题和生成.
结论:
- 对于大型预训练模型的后门攻击,EDT提供了一个可行的解决方案,克服了以前的局限性.
- 该方法提供了一种实际的方法来评估复杂的人工智能系统的脆弱性.
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