相关实验视频
Updated: Jun 16, 2025

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The HoneyComb Paradigm for Research on Collective Human Behavior
Published on: January 19, 2019
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对物理对抗补丁的信息理论视角
Bilel Tarchoun1, Anouar Ben Khalifa2, Mohamed Ali Mahjoub1
1Université de Sousse, Ecole Nationale d'Ingénieurs de Sousse, LATIS- Laboratory of Advanced Technology and Intelligent Systems, 4023, Sousse, Tunisia.
概括
这项研究介绍了Jedi,一种新的防御对抗计算机视觉中的对抗补丁,使用输入来检测. 适应性绝地武士在具有挑战性的图像上提高了高达9%的性能.
科学领域:
- 计算机视觉 计算机视觉
- 信息理论 信息理论
- 机器学习安全 机器学习安全
背景情况:
- 最先进的计算机视觉模型容易受到现实世界的对抗补丁的影响.
- 使用梯度分析的现有防御对自然主义,GAN生成的补丁是无效的.
研究的目的:
- 提出一种基于输入的对抗性补丁的新型防御方法.
- 开发一种适用于现有的计算机视觉模型的模型不可知防御.
主要方法:
- 绝地防御利用信息理论,分析输入来检测敌对补丁.
- 一个自动编码器用于重建被识别为高补丁区域的图像区域.
- 适应的绝地以解决异常来提高性能.
主要成果:
- 绝地实现了高精度的对抗补丁本地化 (90%的平均检测) 和移除 (94%的恢复).
- 防御是模型不可知论的,不需要对现有模型进行修改.
- 适应性绝地将在具有挑战性的图像上提高了高达9%的性能.
结论:
- 输入分析为在物理世界的环境中防御对抗性补丁提供了新的视角.
- 了解故障情况,特别是高异常值,对于强大的防御至关重要.
- 绝地武士和自适应绝地武士为对抗补丁检测和删除提供了有效和多功能解决方案.
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