基于证据的多功能融合为对手的稳定性提供了证据
概括
本研究介绍了基于证据的多功能融合 (EMFF) 以提高深度神经网络 (DNN) 的对抗性. 欧洲货币基金组织量化了功能可信度,防止依赖单一操纵的功能,以改善对攻击的防御.
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) 由于特征空间干扰,容易受到敌对攻击.
- 现有的防御侧重于否定或重新校准特征,但未充分探索特征可信度.
- 依赖单一的,可能被污染的特征阻碍了DNN的稳定性.
研究的目的:
- 提出一种新的方法来量化DNN中的特征可靠性.
- 通过融合多个网络块的可靠功能来增强对抗性稳定性.
- 提高DNN对白盒和黑盒对抗性攻击的弹性.
主要方法:
- 引入基于证据的多功能融合 (EMFF) 以实现对抗性稳定性.
- 使用证据深度学习来量化特征的信念量和不确定性.
- 采用Dempster的规则在跨网络块的可信特征的证据融合.
主要成果:
- 在对抗性强度方面,EMFF在现有防御技术上表现出显著的优势.
- 该方法证明对白盒和黑盒攻击场景都有效.
- 与对抗性训练策略的整合提高了各种架构的稳定性,包括CNN和视觉转换器.
结论:
- 通过可靠地融合值得信赖的特征,EMFF提高了DNN的对抗性稳定性.
- 该方法提供了一个具有成本效益的解决方案,参数增加最小.
- 有证据的深度学习为在对抗性环境中评估特征可靠性提供了强大的框架.
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