Over the past decade, deep learning has revolutionized conventional tasks that rely on hand-
craft feature extraction with its strong feature learning capability, leading to substantial …

In addition to red-green-blue (RGB) camera sensors, light detection and ranging (LiDAR)
plays an important role in autonomous vehicles (AVs) to perceive their surroundings. Deep …

Deep neural networks (DNNs) have found widespread applications in interpreting remote
sensing (RS) imagery. However, it has been demonstrated in previous works that DNNs are …

Although image restoration has achieved significant progress, its potential to assist object
detectors in adverse imaging conditions lacks enough attention. It is reported that the …

Although deep learning has received extensive attention and achieved excellent
performance in various scenarios, it suffers from adversarial examples to some extent. In …

Targeted Attacks on Object Detection (TAOD) aim to deceive the victim detector into
recognizing a specific instance as the predefined target category while minimizing the …

Recent studies have shown that detectors based on deep models are vulnerable to
adversarial examples, even in the black-box scenario where the attacker cannot access the …

Adversarial attacks perturb images such that a deep neural network produces incorrect
classification results. A promising approach to defend against adversarial attacks on natural …

Abstract Although Deep Neural Networks (DNNs)-based object detectors are widely used in
various fields, especially on aerial imagery object detections, it has been observed that a …

Blackbox transfer attacks for image classifiers have been extensively studied in recent years.
In contrast, little progress has been made on transfer attacks for object detectors. Object …