Autonomous driving (AD) has developed tremendously in parallel with the ongoing
development and improvement of deep learning (DL) technology. However, the uptake of …

The existence of real-world adversarial examples (RWAEs)(commonly in the form of
patches) poses a serious threat for the use of deep learning models in safety-critical …

Recent research has shown that deep neural networks are vulnerable to backdoor attacks. A
carefully-designed backdoor trigger will mislead the victim model to misclassify any sample …

Object detectors, which are widely deployed in security-critical systems such as autonomous
vehicles, have been found vulnerable to patch hiding attacks. An attacker can use a single …

Object detection plays an important role in security-critical systems such as autonomous
vehicles but has shown to be vulnerable to adversarial patch attacks. Existing defense …

Adversarial patch attacks present a significant threat to real-world object detectors due to
their practical feasibility. Existing defense methods which rely on attack data or prior …

Person detection has attracted great attention in the computer vision area and is an
imperative element in human-centric computer vision. Although the predictive performances …

With the emergence of foundation models, deep learning-based object detectors have
shown practical usability in closed set scenarios. However, for real-world tasks, object …

Adversarial patches can mislead object detectors to produce erroneous predictions. To
defend against adversarial patches, one can take two types of protections on the model side …

This work presents Z-Mask, an effective and deterministic strategy to improve the adversarial
robustness of convolutional networks against physically-realizable adversarial attacks. The …