图像分类对抗性攻击,改进了大小转换和组合模型
Chenwei Li1,2, Hengwei Zhang1,2, Bo Yang1,2
1State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, Henan, China.
PeerJ. Computer science
|August 7, 2023
概括
这项研究增强了使用模型增强和组合模型的黑子攻击中的对抗性示例可转移性. 拟议的调整大小的不变性方法提高了对各种模型的对抗性攻击成功率.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 卷积神经网络 (CNN) 在计算机视觉方面很强大,但对对抗性示例很脆弱.
- 敌对的例子,不可察觉的输入扰动,突出显示CNN漏洞,并用于评估网络稳定性.
- 与白盒攻击相比,黑盒攻击的成功率较低,可转移性有限.
研究的目的:
- 为了提高黑子攻击中对抗性示例的成功率和可转移性.
- 引入一种新的模型增强技术,用于生成更强大的对抗性示例.
- 加强神经网络对复杂的对抗性攻击的安全评估.
主要方法:
- 提出了一个调整尺寸的不变性方法用于模型增强,灵感来自数据增强技术.
- 利用改进的调整尺寸转换来增强模型增强功能.
- 采用集体模型来生成具有更高可转移性的对抗性示例.
主要成果:
- 拟议的调整大小的不变性方法与基线方法相比显示出更高的性能.
- 在正常和防御模型中,在黑子攻击成功率方面取得了显著的改进.
- 验证了集合模型在生成更多可转移的对抗性示例方面的有效性.
结论:
- 调整尺寸的不变性方法是模型增强在对抗性攻击的背景下有效的方法.
- 提出的技术增强了对抗性示例的可转移性,对模型安全构成更大的挑战.
- 这项研究有助于更好地理解和评估神经网络对抗对抗干扰的稳定性.
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