互插联合太空对抗训练,为强大和可通用防御提供训练
概括
反对训练 (AT) 可能会损害标准准确性. 本研究介绍了联合空间威胁模型 (JSTM) 和强大的混合,提高了模型的强度和对未见的攻击的概括性.
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:
- 对抗训练 (AT) 是对抗攻击的关键防御,但往往会降低标准准确性和对新威胁的概括性.
- 改善概括的现有方法,如在多重或神经感知威胁模型,在数据要求或算法复杂性方面存在局限性.
研究的目的:
- 引入一个新的联合空间威胁模型 (JSTM),有效地利用多种信息.
- 在JSTM下开发新的对抗性攻击和防御,特别是强大的混合战略.
- 在标准准确性,稳定性和概括性方面提高模型性能.
主要方法:
- 提出了联合空间威胁模型 (JSTM),利用规范化流来利用多种信息.
- 开发了强大的混合策略,以最大限度地提高插入图像中的逆境,增强强度并防止过拟合.
- 作为一种新的防御机制,实施了互插联合太空对抗训练 (IJSAT).
主要成果:
- 在标准准确性,稳定性和跨基准数据集 (CIFAR-10/100,OM-ImageNet,CIFAR-10-C) 的概括性方面,IJSAT表现出卓越的性能.
- 强大的混合战略有效地提高了对手的强度,并防止了过度装配.
- IJSAT证明了灵活性,作为数据增强技术和增强现有的AT方法.
结论:
- 拟议的JSTM和IJSAT在对抗性稳定性和通用性方面取得了重大进展.
- IJSAT提供了一种多功能解决方案,可以与现有方法集成,以提高性能.
- 这项工作解决了当前对抗性训练防御的关键局限性,为更具弹性的人工智能模型铺平了道路.
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