论文标题
通过知识图一致性进行有效的多标签识别攻击
Towards Effective Multi-Label Recognition Attacks via Knowledge Graph Consistency
论文作者
论文摘要
图像识别的许多真实应用都需要多标签学习，其目标是在图像中找到所有标签。因此，这种系统对对抗图像扰动的鲁棒性非常重要。但是，尽管最近对对抗性攻击进行了大量研究，但现有作品的范围主要限于多级设置，每个图像都包含一个单个标签。我们表明，多级攻击对多标签设置的幼稚扩展导致违反标签关系，以知识图建模，并且可以使用一致性验证方案检测到。因此，我们提出了一个符合图形的多标签攻击框架，该框架搜索了小图像扰动，从而导致在尊重标签层次结构时错误地分类所需的目标集。通过在两个数据集上进行广泛的实验，并使用多个多标签识别模型，我们表明我们的方法会产生非常成功的攻击，这些攻击与幼稚的多标签扰动不同，可以产生与知识图一致的模型预测。
Many real-world applications of image recognition require multi-label learning, whose goal is to find all labels in an image. Thus, robustness of such systems to adversarial image perturbations is extremely important. However, despite a large body of recent research on adversarial attacks, the scope of the existing works is mainly limited to the multi-class setting, where each image contains a single label. We show that the naive extensions of multi-class attacks to the multi-label setting lead to violating label relationships, modeled by a knowledge graph, and can be detected using a consistency verification scheme. Therefore, we propose a graph-consistent multi-label attack framework, which searches for small image perturbations that lead to misclassifying a desired target set while respecting label hierarchies. By extensive experiments on two datasets and using several multi-label recognition models, we show that our method generates extremely successful attacks that, unlike naive multi-label perturbations, can produce model predictions consistent with the knowledge graph.
