个人和共同的攻击:通过模式特征利用,提高VLP模型的可转移性.
概括
本研究引入了一个新的对抗性攻击框架,I&CA,以提高对视觉语言预训练 (VLP) 模型的攻击的可转移性. 通过针对单个和共同的特征来实现更有效的多式联络攻击,I&CA增强了对抗性强度.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 自然语言处理自然语言处理.
背景情况:
- 视觉语言预训练 (VLP) 模型在多式联络任务中表现出色,但容易受到对抗性攻击.
- 目前的攻击方法由于过度适应单个模式特征而受到有限的可转移性.
研究的目的:
- 提出一个新的对抗性攻击框架,I&CA (个人和共同特征攻击),以提高对抗性示例在VLP模型中的可转移性.
- 通过解决它们对对抗性操纵的脆弱性,提高VLP模型在安全关键应用中的可靠性.
主要方法:
- I&CA同时考虑每个模式的个体特征和跨模式的共同特征.
- 该框架通过诱导个体特征之间的分歧来破坏单一模式学习.
- 它在交叉模式交互期间抑制共同特征表达,并为两种模式采用增强策略.
主要成果:
- 与最先进的DRA方法相比,I&CA框架的平均可转移性改善为6.15%.
- 在跨模型和跨任务攻击场景中都观察到显著的性能增长.
- 提出的方法有效地提高了对抗性示例与VLP模型的可转移性.
结论:
- I&CA提供了一种更强大的方法,通过考虑单个和共同的特征,为VLP模型制作对抗性示例.
- 该框架提高可转移性的能力解决了现有方法的主要局限性.
- 这项研究有助于理解和减轻多式联运人工智能系统中的对抗性漏洞.
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