对于不可察觉和可转移的对抗性攻击的扩散模型
IEEE transactions on pattern analysis and machine intelligence
|October 15, 2024
概括
本研究介绍了DiffAttack,这是一种使用扩散模型创建视觉上不可察觉的对抗性攻击的新方法. DiffAttack增强了对黑子模型的可转移性,优于现有技术.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 现有的对抗性攻击往往会在图像RGB空间中产生明显的干扰.
- 不受限制的攻击缺乏可转移到黑子模型的可能性.
- 视觉不可感知性和可转移性是对抗性攻击中的关键挑战.
研究的目的:
- 提出一种新的对抗性攻击方法,DiffAttack,可以实现视觉不可察觉性和可转移性.
- 为了利用传播模型的生成和歧视能力,为制造对抗性示例.
- 解决现有的对抗性攻击策略的局限性.
主要方法:
- 在扩散模型的潜在空间中制造干扰,而不是像素空间.
- 使用内容保存结构来产生人类不敏感的扰动,用语义线索.
- 通过分散他们的注意力来欺骗扩散模型,以提高可转移性.
主要成果:
- DiffAttack生成不可察觉的对抗性示例,这些示例可以在各种模型架构 (CNN,变压器,MLP) 中转移.
- 拟议的方法证明了它在代性,基于GAN和整体攻击方面的优势.
- 在ImageNet,CUB-200和斯坦福汽车数据集上进行了实验,验证了对各种防御的有效性.
结论:
- DiffAttack是第一个将扩散模型整合到对抗性攻击中的方法,提供了一个新的方向.
- 这种方法成功地平衡了不可察觉性和可转移性,优于以前的方法.
- 未来的研究可以探索基于扩散的对抗性攻击的进一步进展.
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