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FP-ZOO:基于快速补丁的零顺序优化,用于黑盒对视觉模型的对抗性攻击
1Telecommunications Technology Association, Bundang-ro 47, Bundang-gu, Seongnam-si 13591, Gyeonggi-do, Republic of Korea.
Sensors (Basel, Switzerland)
|November 27, 2025
概括
一个新的基于补丁的快速零顺序优化 (FP-ZOO) 攻击增强了深度视觉模型的对抗性强度. FP-ZOO实现了高成功率和更快的生成时间来抵御逃避攻击,改善了模型的安全性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 深度神经网络 (DNN) 在视觉任务中表现出色,但容易受到对抗性攻击.
- 逃避攻击,特别是在黑盒设置中,威胁到现实应用中的DNN可靠性.
- 现有的方法,如零次顺序优化 (ZOO),由于效率低下和内存复杂性,难以处理高分辨率图像.
研究的目的:
- 提出一种新的基于补丁的快速零顺序优化 (FP-ZOO) 攻击.
- 为了解决现有的动物园攻击高分辨率图像的局限性.
- 提高黑盒规避攻击对视觉模型的效率和成功率.
主要方法:
- FP-ZOO将图像分割成针对性扰动的补丁.
- 它使用基于概率的采样和epsilon-greedy调度策略来有效地产生干扰.
- 对CIFAR-10,CIFAR-100和ImageNet数据集进行了大规模的评估.
主要成果:
- 在非目标攻击中,FP-ZOO在ImageNet上实现了97-100%的攻击成功率.
- 这次攻击的生成时间比标准的动物园攻击快10秒.
- 在有针对性的攻击中,FP-ZOO的性能相对较低,这表明了未来研究的领域.
结论:
- 对于针对视觉模型的非定向回避攻击,FP-ZOO可显著提高效率和有效性.
- 拟议的方法通过克服先前动物园攻击的局限性,提高了对手的稳定性.
- 需要进一步的研究来优化FP-ZOO针对目标攻击场景.
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