Towards Effective Multi-Label Recognition Attacks via Knowledge Graph Consistency

• URL: http://arxiv.org/abs/2207.05137v1
• Date: Mon, 11 Jul 2022 19:08:32 GMT
• Title: Towards Effective Multi-Label Recognition Attacks via Knowledge Graph Consistency
• Authors: Hassan Mahmood, Ehsan Elhamifar
• Abstract summary: We show that the naive extensions of multi-class attacks to the multi-label setting lead to violating label relationships. We propose a graph-consistent multi-label attack framework, which searches for small image perturbations that lead to misclassifying a desired target set.
• Score: 33.250544869840155
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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