Thermal infrared detectors have a vast array of potential applications in pedestrian detection
and autonomous driving, and their safety performance is of great concern. Recent works use …

Physical adversarial attacks directly apply adversarial perturbations to real-world objects.
Perturbations usually are printed as patches and pasted on target objects. This requires …

Studies have proven that applying patch stickers generated through adversarial training to
target objects can effectively deceive classifiers or target detectors. These'Print-and …

Deep neural network security is a persistent concern, with considerable research on visible
light physical attacks but limited exploration in the infrared domain. Existing approaches, like …

Autonomous vehicles (AVs) empowered by deep neural networks (DNNs) are bringing
transformative changes to our society. However, they are generally susceptible to …

While text-based CAPTCHAs have been the predominant type of human interaction proofs
(HIPs) for many years, image recognition challenges have also gained significant attention …

Currently, infrared imaging technology enjoys widespread usage, with infrared object
detection technology experiencing a surge in prominence. While previous studies have …

Recent studies have demonstrated that finely tuned deep neural networks (DNNs) are
susceptible to adversarial attacks. Conventional physical attacks employ stickers as …

Adversarial attacks involve introducing minimal perturbations into the original input to
manipulate deep learning models into making incorrect network predictions. Despite …

Polarization is a fundamental property of light that encodes abundant information regarding
surface shape material illumination and viewing geometry. The computer vision community …