It has been recognized that the data generated by the denoising diffusion probabilistic
model (DDPM) improves adversarial training. After two years of rapid development in …

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 …

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 …

Deep learning methods can not only detect false data injection attacks (FDIA) but also locate
attacks of FDIA. Although adversarial false data injection attacks (AFDIA) based on deep …

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 …

With the wide application of remote sensing images (RSIs) in military and civil fields, remote
sensing object detection (RSOD) has gradually become a hot research direction. However …

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 …

Patch-based physical attacks have increasingly aroused concerns. However, most existing
methods focus on obscuring targets captured on the ground, and some of these methods are …

The Self-Attention Mechanism (SAM) excels at distilling important information from the
interior of data to improve the computational efficiency of models. Nevertheless, many …

Adversarial attacks serve as an efficient approach to investigating model robustness,
providing insights into internal weaknesses. In real-world applications, the model …