The rapid development and widespread application of Unmanned Aerial Vehicles (UAV) have raised significant concerns about safety and privacy, thus requiring powerful anti-UAV systems. This survey provides an overview of anti-UAV detection and tracking methods in recent years. Firstly, we emphasize the key challenges of existing anti-UAV and delve into various detection and tracking methods. It is noteworthy that our study emphasizes the shift toward deep learning to enhance detection accuracy and tracking performance. Secondly, the survey organizes some public datasets, provides effective links, and discusses the characteristics and limitations of each dataset. Next, by analyzing current research trends, we have identified key areas of innovation, including the progress of deep learning techniques in real-time detection and tracking, multi-sensor fusion systems, and the automatic switching mechanisms that adapt to different conditions. Finally, this survey discusses the limitations and future research directions. This paper aims to deepen the understanding of innovations in anti-UAV detection and tracking methods. Hopefully our work can offer a valuable resource for researchers and practitioners involved in anti-UAV research.

Adversarial patch attacks are core tools to evaluate adversarial robustness in remote sensing image classification, yet existing methods with fixed patch shapes and single-model optimization yield poor cross-architecture black-box transferability. This paper proposes the Dynamic Patch Shape Selection Framework (DPSF), which integrates a 12-shape patch library, adaptive category matching and multi-model joint optimization with dynamic weight adjustment under a strict 2% patch area constraint. We test DPSF on the UC Merced dataset over eight random split seeds, taking ResNet101, DenseNet161 and VGG19 as held-out black-box models. Under the five-source protocol, DPSF-full achieves mean accuracy drop (${\mathrm{ACC}}_D$) of $0.380 \pm 0.034$, adversarial accuracy (Adv Acc) $0.593 \pm 0.039$, attack success rate (ASR) $0.393 \pm 0.037$ (on clean-correct test samples, averaged over held-out evaluators), union success rate (USR) $0.581 \pm 0.046$, and intersection success rate (ISR) $0.188 \pm 0.023$. With the ResNet50 single-source setting, DPSF-single obtains mean ${\mathrm{ACC}}_D = 0.275 \pm 0.109$, surpassing Sparse-RS ($0.033 \pm 0.012$) and Cross-shaped adversarial patch attack (CSPA) ($0.232 \pm 0.029$). Supplementary results on CIFAR-10, Tiny-ImageNet and Mini-ImageNet illustrate cross-scale transfer trends. This work builds a reproducible digital-domain robustness benchmark for aerial land-use scene classification.