增加边际对抗性 (IMA) 培训,以提高神经网络的对抗性强度
1Department of Computer Science, University of Miami, 1365 Memorial Drive, Coral Gables, 33146, FL, USA.
Computer methods and programs in biomedicine
|July 1, 2023
概括
我们推出增加边际对抗 (IMA) 培训,以提高深度神经网络对抗对抗噪声的稳定性. 这种新的方法避免了典型的权衡,保持了标准准确性,同时增强了图像分类和细分的防御能力.
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 机器学习
背景情况:
- 深度神经网络 (DNN) 容易受到对抗噪音的影响,这会影响它们的可靠性.
- 敌对训练提高了DNN的稳定性,但通常会降低标准准确性,这种现象被称为准确性-稳定性权衡.
- 这种权衡限制了对抗训练在医学图像分析等关键领域的应用.
研究的目的:
- 开发一种新的对抗性训练方法,以减轻或避免准确性-稳定性权衡.
- 为了提高医疗图像分类和细分任务的标准准确性和对抗性稳定性.
主要方法:
- 建议增加边际对抗性 (IMA) 培训,这是一种基于对抗性培训样本平衡状态分析的新方法.
- 生成最佳的对抗性训练样本,以保持准确性,同时提高稳定性.
- 通过使用AutoAttack和白噪声攻击对六个公共图像数据集的八种其他方法进行IMA培训的评估.
主要成果:
- IMA培训在图像分类和细分方面实现了卓越的对抗性稳定性.
- 与其他方法相比,该方法显示了标准准确度的最小减少.
- 在特定的应用中,IMA培训同时提高了准确性和稳定性.
结论:
- 拟议的IMA培训方法成功地消除了图像分类和细分中的准确性-稳定性权衡.
- 这项研究提供了第一个证据,证明准确性-稳定性权衡在医疗图像细分方面是可以避免的.
- IMA培训为在敏感领域部署强大的DNN提供了一个有前途的解决方案,而不会影响性能.
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