Recent advances in artificial intelligence (AI) have significantly intensified research in the
geoscience and remote sensing (RS) field. AI algorithms, especially deep learning-based …

Visual attention has become a popular and widely used component for image recognition.
Although various attention-based methods have been proposed and achieved relatively …

Advanced patch attacks (PAs) on object detection in natural images have pointed out the
great safety vulnerability in methods based on deep neural networks (DNNs). However, little …

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 …

Abstract Deep Neural Networks show superior performance in various tasks but are
vulnerable to adversarial attacks. Most defense techniques are devoted to the adversarial …

On account of the extreme settings, stealing the black-box model without its training data is
difficult in practice. On this topic, along the lines of data diversity, this paper substantially …

Most existing adversarial attack methods against detectors involve adding adversarial
perturbations to benign images to synthesize adversarial examples. However, directly …

Recent years have witnessed the great success of deep learning algorithms in the
geoscience and remote sensing (RS) realm. Nevertheless, the security and robustness of …

Known as a hard nut, the single-model transferable targeted attacks via decision-level
optimization objectives have attracted much attention among scholars for a long time. On …

Numerous works study black-box attacks on image classifiers, where adversaries generate
adversarial examples against unknown target models without having access to their internal …