Nowadays, users upload numerous photos to social network platforms to share their daily
lives. These photos contain numerous personal information, which can be easily captured …

Recent studies have shown that deep learning-based classifiers are vulnerable to malicious
inputs, ie, adversarial examples. A practical solution is to construct a perceptible but …

Siamese trackers are shown to be vulnerable to adversarial attacks recently. However, the
existing attack methods craft the perturbations for each video independently, which comes at …

In recent years, the application of video Internet of Things (IoT) in various cities and public
places has brought unprecedented opportunities to the security field and achieved great …

Adversarial attacks have been recently investigated in person re-identification. These
attacks perform well under cross dataset or cross model setting. However, the challenges …

The research on the single image dehazing task has been widely explored. However, as far
as we know, no comprehensive study has been conducted on the robustness of the well …

Due to the gap between a substitute model and a victim model, the gradient-based noise
generated from a substitute model may have low transferability for a victim model since their …

Recent studies highlight the vulnerability of convolutional neural networks (CNNs) to
adversarial attacks, which also calls into question the reliability of forensic methods. Existing …

Deep neural networks can be easily fooled by the adversarial example, which is a specially
crafted example with subtle and intentional perturbations. A plethora of papers have …

Adversarial attacks pose a huge challenge to the deployment of deep neural networks
(DNNs) in security-sensitive applications. Adversarial defense methods are developed to …