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A06B-6114-H103
近引起研究界注意的一个问题是这些系统对对抗样本的敏感性。一个对抗性的例子是一个嘈杂的图像,旨在欺骗系统做出错误的预测。为了在现实中部署这些系统,它们能够检测到这些示例。为此,近的工作探索了通过在训练过程中包含对抗性示例来使这些系统更强对抗性攻击的可能性。
现阶段对模型攻击的分类主要分为两大类,即攻击训练阶段和推理阶段。
训练阶段的攻击
训练阶段的攻击 (Training in Adversarial Settings) ,主要的方法就是针对模型的参数进行微小的扰动,从而达到让模型的性能和预期产生偏差的目的。例如直接通过对于训练数据的标签进行替换,让数据样本和标签不对应,从而后训练的结果也与预期的产生差异,或者通过在线的方式获得训练数据的输入权,操纵恶意数据来对在线训练过程进行扰动,后的结果就是产出脱离预期。
A06B-6114-H103
A06B-6114-H103
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