针对安全机器学习模型的稳定性一致对抗训练 更新
概括
模型更新可能会降低性能,这个问题被称为负翻转. 本研究引入了强度一致的对抗训练,以在机器学习模型更新期间保持安全性和准确性,防止性能回归.
科学领域:
- 机器学习 机器学习
- 计算机视觉 计算机视觉
- 网络安全 网络安全
背景情况:
- 机器学习模型需要定期更新以提高使用新数据和架构的精度.
- 模型更新可以引入"负翻转",新模型在以前正确的输入上表现更差,降低用户体验.
- 这些负面翻转也会影响对抗性稳定性,破坏安全的模型更新实践.
研究的目的:
- 在模型更新期间调查负翻转对对抗性稳定性的影响.
- 提出一种新的方法,强度一致的对抗训练,以减轻对抗强度中的性能回归.
- 建立一个理论框架,用于培训使用非回归约束的一致估计者.
主要方法:
- 使用对抗训练微调机器学习模型.
- 在更新之前对未受到对抗性攻击影响的样本实施限制,以保持高稳定性.
- 开发一个理论上有基础的框架,用于学习的非回归约束.
主要成果:
- 负翻转会影响准确性和对抗性强度,即使更新后整体性能有所改善.
- 强度一致的对抗训练有效地减轻了对抗强度的负面反转.
- 拟议的方法在保持一致的性能和安全性方面优于现有的基线方法.
结论:
- 由于负翻转,模型更新带来了重大挑战,影响了一般准确性和对抗性安全性.
- 稳定性一致的对抗训练提供了一个可行的解决方案,以防止模型更新期间的性能回归.
- 具有非回归约束的学习框架为开发更可靠的机器学习模型提供了理论上合理的方法.
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