Collapse-Aware Triplet Decoupling for Adversarially Robust Image Retrieval

• URL: http://arxiv.org/abs/2312.07364v4
• Date: Thu, 6 Jun 2024 15:24:15 GMT
• Title: Collapse-Aware Triplet Decoupling for Adversarially Robust Image Retrieval
• Authors: Qiwei Tian, Chenhao Lin, Zhengyu Zhao, Qian Li, Chao Shen,
• Abstract summary: Adversarial training has achieved substantial performance in defending image retrieval against adversarial examples. Existing studies in deep metric learning (DML) still suffer from two major limitations: weak adversary and model collapse. We propose Collapse-Aware TRIplet DEcoupling (CA-TRIDE) to yield a stronger adversary.
• Score: 12.007316506425079
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 stronger 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. Codes are available at https://github.com/michaeltian108/CA-TRIDE.

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