CgAT: Center-Guided Adversarial Training for Deep Hashing-Based Retrieval. (arXiv:2204.10779v2 [cs.CV] UPDATED)

Deep hashing has been extensively utilized in massive image retrieval because
of its efficiency and effectiveness. However, deep hashing models are
vulnerable to adversarial examples, making it essential to develop adversarial
defense methods for image retrieval. Existing solutions achieved limited
defense performance because of using weak adversarial samples for training and
lacking discriminative optimization objectives to learn robust features. In
this paper, we present a min-max based Center-guided Adversarial Training,
namely CgAT, to improve the robustness of deep hashing networks …

adversarial center hashing training