Although deep learning has received extensive attention and achieved excellent performance in various scenarios, it suffers from adversarial examples to some extent. In particular, physical attack poses a greater threat than digital attack. However, existing research has paid less attention to the physical attack of object detection in UAV remote sensing images (RSIs). In this work, we carefully analyze the universal adversarial patch attack for multi-scale objects in the field of remote sensing. There are two challenges faced by an adversarial attack in RSIs. On one hand, the number of objects in remote sensing images is more than that of natural images. Therefore, it is difficult for an adversarial patch to show an adversarial effect on all objects when attacking a detector of RSIs. On the other hand, the wide height range of the photography platform causes the size of objects to vary a great deal, which presents challenges for the generation of universal adversarial perturbation for multi-scale objects. To this end, we propose an adversarial attack method of object detection for remote sensing data. One of the key ideas of the proposed method is the novel optimization of the adversarial patch. We aim to attack as many objects as possible by formulating a joint optimization problem. Furthermore, we raise the scale factor to generate a universal adversarial patch that adapts to multi-scale objects, which ensures that the adversarial patch is valid for multi-scale objects in the real world. Extensive experiments demonstrate the superiority of our method against state-of-the-art methods on YOLO-v3 and YOLO-v5. In addition, we also validate the effectiveness of our method in real-world applications.

Seismic exploration is a crucial method for studying underground geological structures and oil/gas resources. However, the presence of various noise sources during seismic wave propagation hinders accurate interpretation and imaging. To address this challenge, effective denoising methods are essential. In recent years, deep learning, particularly Convolutional Neural Networks (CNNs), has shown promise in seismic data processing. Nevertheless, CNNs have limitations in capturing long-range dependencies and global coherence. As an alternative, we propose a Transformer-based model called Seismic Data Denoising Transformer (SDT) for seismic signal processing. By leveraging self-attention mechanisms, the SDT model overcomes the limitations of CNNs and effectively captures long-range features for seismic signal reconstruction. We also introduce a novel self-supervised pretraining strategy using a large-scale dataset to further enhance performance. Experimental results demonstrate the advantages of SDT in complex seismic noise attenuation and preserving weak signal amplitudes. The proposed method exhibits promising potential for real-world seismic data applications.

In modern seismic data acquisition, real-time data collection is a challenging task due to bandwidth limitations in wireless communications. In this letter, we propose a novel compressive data gathering scheme using generative adversarial networks, named GAN-CDG, to improve the efficiency of data gathering. Instead of collecting the originally acquired data, GAN-CDG gathers data projections in wireless geophone networks. Data compression and load-balanced relay transmission are utilized during the projection process. To speed up the formation of projections, the shortest path routing tree (SPRT) is constructed, which achieves the minimum end-to-end time delay. The sparse domain of seismic signals and its reconstruction mapping are learned by sparsity-constrained adversarial networks. The testing results demonstrate that projections with high compression ratios (e.g., 16) are gathered efficiently with the SPRT. Then, original seismic signals can be reconstructed accurately (over 30 dB) from the projections using the adversarial model, which outperforms the state-of-the-art method.

Many deep learning methods have been proposed to classify moving targets from seismic signals in recent years. However, the existing deep models are all designed based on the “end-cloud” framework, in which real-time data processing is difficult because of communication delays. To address this problem and achieve on-site target classification, we propose a novel edge intelligence-oriented method, named compressed sensing-edge convolutional neural network (CS-ECNN). In this method, the acquired seismic signals are first mapped onto a compressed domain using CS. This operation reduces data dimensions, while being able to retain the vast majority of valuable seismic features. Following that, a convolutional neural network is employed to extract implicit features directly from the compressed seismic measurements and then classify the feature vectors. To evaluate the proposed method, the seismic data recorded in DARPA’s SensIT project are used as a case study. The experimental results demonstrate that the proposed model is edge-matched, and it achieves comparable classification accuracy to the state-of-the-art cloud-based models with only 1/10 computation time.

Due to the strong nonstationary characteristics of seismic signals, energy criteria-based methods are not robust for detecting moving targets, especially in data with low SNRs. To address this problem, we propose a new method for detecting ground moving target based on fractal dimension (FD) theory named FD-based support vector machine (FD-SVM). In this method, seismic signals are first measured by fractals, which can effectively extract seismic nonlinear features. These fractal features are then fed into an SVM to distinguish moving targets from noise. Two data sets are used to evaluate the proposed method. One is a set of seismic signals induced by wheeled and tracked vehicles. The other is a set of seismic signals generated by human footsteps. Experimental results demonstrate that the proposed FD-SVM algorithm achieves promising results on both data sets. Compared with the benchmark methods, the FD-SVM algorithm achieves a better precision rate, recall rate, and F1 score.