Collapse-Aware Triplet Decoupling for Adversarially Robust Image Retrieval
CoRR(2023)
摘要
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.
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