  Deep Neural Network classifiers are vulnerable to adversarial attack, where
an imperceptible perturbation could result in misclassification. However, the
vulnerability of DNN-based image ranking systems remains under-explored. In
this paper, we propose two attacks against deep ranking systems, i.e.,
Candidate Attack and Query Attack, that can raise or lower the rank of chosen
candidates by adversarial perturbations. Specifically, the expected ranking
order is first represented as a set of inequalities, and then a triplet-like
objective function is designed to obtain the optimal perturbation. Conversely,
an anti-collapse triplet defense is proposed to improve the ranking model
robustness against all proposed attacks, where the model learns to prevent the
positive and negative samples being pulled close to each other by adversarial
attack. To comprehensively measure the empirical adversarial robustness of a
ranking model with our defense, we propose an empirical robustness score, which
involves a set of representative attacks against ranking models. Our
adversarial ranking attacks and defenses are evaluated on MNIST, Fashion-MNIST,
CUB200-2011, CARS196 and Stanford Online Products datasets. Experimental
results demonstrate that a typical deep ranking system can be effectively
compromised by our attacks. Nevertheless, our defense can significantly improve
the ranking system robustness, and simultaneously mitigate a wide range of
attacks.
