Collapse-Oriented Adversarial Training with Triplet Decoupling for Robust Image Retrieval

Qiwei Tian, Chenhao Lin, Qian Li, Zhengyu Zhao, Chao Shen

Abstract: Adversarial training has achieved substantial performance in defending image retrieval against adversarial examples. However, existing studies in deep metric learning (DML) still suffer from two major limitations: weak adversary and model collapse. In this paper, we address these two limitations by proposing collapse-aware triplet decoupling (CA-TRIDE). Specifically, TRIDE yields a strong adversary by spatially decoupling the perturbation targets into the anchor and the other candidates. Furthermore, CA prevents the consequential model collapse, based on a novel metric, collapseness, which is incorporated into the optimization of perturbation. We also identify two drawbacks of the existing robustness metric in image retrieval and propose a new metric for a more reasonable robustness evaluation. Extensive experiments on three datasets demonstrate that CA-TRIDE outperforms existing defense methods in both conventional and new metrics.