查询效率高的基于决策的对抗性攻击,查询预算低
Yangzhuo Tuo1, Mengge Yin1, Shengbing Che2
1School of Electronic Information and Physics, Central South University of Forestry and Technology, Changsha, 410004, China.
Scientific reports
|February 2, 2026
概括
这项研究引入了一种新的基于决策的攻击,称为One Plane One Query Attack (OPOQA),以高效地为深度神经网络创建对抗示例. 通过更少的查询,OPOQA显著提高了攻击成功率,从而增强了模型安全研究.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 机器学习安全 机器学习安全
背景情况:
- 深度神经网络 (DNN) 实现了高性能,但容易受到对抗的例子的影响.
- 由于对模型内部的访问有限,现实世界的模型攻击具有挑战性,因此基于决策的攻击至关重要.
- 现有的基于决策的攻击需要大量的查询,这限制了它们的实际应用.
研究的目的:
- 开发一种新的,高效的基于决策的攻击方法,用于生成高质量的对抗性示例.
- 为了减少对DNN.N的成功对抗性攻击所需的查询预算.
- 增强DNN的安全分析,防止对手操纵.
主要方法:
- 引入了一个平面一个查询攻击 (OPOQA) 方法.
- 采用了一个从低频离散等号变换 (DCT) 空间采样的2D平面内生成候选示例的策略.
- 利用反向DCT (iDCT) 将样本转换回像素空间,每次代只查询一个候选项以尽量减少查询数量.
主要成果:
- 在ImageNet数据集上,在Inception-v3,ResNet和VGG等主流模型上实现了高的攻击成功率.
- 与最先进的EPCA算法相比,表现出优越的性能,在RMSE值分别为0.05和0.01时,攻击成功率增加了4.5%和11.5%.
- 在一个低查询预算 (1000个查询) 中,成功生成了几乎不可察觉的对抗性示例.
结论:
- OPOQA是一种有效和高效的基于决策的攻击,用于生成高质量的对抗性示例.
- 拟议的方法大大降低了对抗性攻击的查询复杂性,使其更实用.
- 这项研究有助于理解和提高深度神经网络的安全稳定性.
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