Evasion attack in multi-label learning systems is an interesting, widely
witnessed, yet rarely explored research topic. Characterizing the crucial
factors determining the attackability of the multi-label adversarial threat is
the key to interpret the origin of the adversarial vulnerability and to
understand how to mitigate it. Our study is inspired by the theory of
adversarial risk bound. We associate the attackability of a targeted
multi-label classifier with the regularity of the classifier and the training
data distribution. Beyond the theoretical attackability analysis, we further
propose an efficient empirical attackability estimator via greedy label space
exploration. It provides provably computational efficiency and approximation
accuracy. Substantial experimental results on real-world datasets validate the
unveiled attackability factors and the effectiveness of the proposed empirical
attackability indicator

在多标签学习系统中，我们研究了逃避攻击这个有趣但鲜为人知的研究课题，通过使用贪婪标签空间探索，我们进一步提出了一种有效的经验攻击性估计器，它具有可证明的计算效率和近似精度。实验结果验证了揭示的攻击能力因素和拟议的经验攻击性指标的有效性。