  Deep hashing has been intensively studied and successfully applied in
large-scale image retrieval systems due to its efficiency and effectiveness.
Recent studies have recognized that the existence of adversarial examples poses
a security threat to deep hashing models, that is, adversarial vulnerability.
Notably, it is challenging to efficiently distill reliable semantic
representatives for deep hashing to guide adversarial learning, and thereby it
hinders the enhancement of adversarial robustness of deep hashing-based
retrieval models. Moreover, current researches on adversarial training for deep
hashing are hard to be formalized into a unified minimax structure. In this
paper, we explore Semantic-Aware Adversarial Training (SAAT) for improving the
adversarial robustness of deep hashing models. Specifically, we conceive a
discriminative mainstay features learning (DMFL) scheme to construct semantic
representatives for guiding adversarial learning in deep hashing. Particularly,
our DMFL with the strict theoretical guarantee is adaptively optimized in a
discriminative learning manner, where both discriminative and semantic
properties are jointly considered. Moreover, adversarial examples are
fabricated by maximizing the Hamming distance between the hash codes of
adversarial samples and mainstay features, the efficacy of which is validated
in the adversarial attack trials. Further, we, for the first time, formulate
the formalized adversarial training of deep hashing into a unified minimax
optimization under the guidance of the generated mainstay codes. Extensive
experiments on benchmark datasets show superb attack performance against the
state-of-the-art algorithms, meanwhile, the proposed adversarial training can
effectively eliminate adversarial perturbations for trustworthy deep
hashing-based retrieval. Our code is available at
https://github.com/xandery-geek/SAAT.
