With the extensive application of deep learning, adversarial attacks especially query-based
attacks receive more concern than ever before. However, the scenarios assumed by existing …

We study the query-based attack against image retrieval to evaluate its robustness against
adversarial examples under the black-box setting, where the adversary only has query …

Most existing works of adversarial samples focus on attacking image recognition models,
while little attention is paid to the image retrieval task. In this paper, we identify two inherent …

There is an increasing interest in studying adversarial attacks on image retrieval systems.
However, most of the existing attack methods are based on the white-box setting, where the …

Universal adversarial perturbations (UAPs), aka input-agnostic perturbations, has been
proved to exist and be able to fool cutting-edge deep learning models on most of the data …

This paper aims to craft adversarial queries for image retrieval, which uses image features
for similarity measurement. Many commonly used methods are developed in the context of …

Abstract Deep Neural Network (DNN) classifiers are vulnerable to adversarial attack, where
an imperceptible perturbation could result in misclassification. However, the vulnerability of …

Adversarial training has achieved substantial performance in defending image retrieval
systems against adversarial examples. However, existing studies still suffer from two major …

Abstract Key Smart City applications such as traffic management and public security rely
heavily on the intelligent processing of video and image data, often in the form of visual …

This paper develops a certified defense for deep neural network (DNN) based content
based image retrieval (CBIR) against adversarial examples (AXs). Previous works put their …