多标签的对抗性攻击与新的措施和自律的约束权衡
概括
本研究介绍了在多标签学习中针对攻击失败度 (AFD) 和攻击成本 (AC) 的精细测量. 一种新的自动加权策略可以提高对手攻击的收益,同时确保优化稳定性.
科学领域:
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 多标签学习中的对抗性攻击涉及复杂的优化问题.
- 现有的方法在攻击失败度 (AFD) 和攻击成本 (AC) 的粗略测量方面扎,特别是与相互冲突的约束因素.
研究的目的:
- 在top-k对抗性攻击中为AFD和AC开发精细的措施.
- 制定解决约束违规问题的新型优化问题.
- 为提高攻击性能和稳定性提出自动节奏加权策略.
主要方法:
- 为AFD和AC开发了一个基于Jaccard指数的衡量标准.
- 制定了优化问题,使用新的AFD/AC措施将约束违规最小化.
- 实施了自动步调权重策略,以优先考虑约束.
主要成果:
- 杰卡德指数衡量更好地区分攻击失败程度和成本.
- 权重松变量从理论上来说可以改善优化结果.
- 自动节奏加权策略产生更大的攻击收益,并避免优化波动.
结论:
- 拟议的精细措施和自动步调权重策略加强了多标签学习中的对抗性攻击.
- 该方法在基准数据集中表现出卓越的性能和稳定性.
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