With the adversarial attack of convolutional neural networks (CNNs), we are able to generate adversarial patches to make an aircraft undetectable by object detectors instead of covering the aircraft with large camouflage nets. However, aircraft in remote sensing images (RSIs) have the problem of large variations in scale, which can easily cause size mismatches between an adversarial patch and an aircraft. A small adversarial patch has no attack effect on large aircraft, and a large adversarial patch will completely cover small aircraft so that it is impossible to judge whether the adversarial patch has an attack effect. Therefore, we propose the adversarial attack method Patch-Noobj for the problem of large-scale variation in aircraft in RSIs. Patch-Noobj adaptively scales the width and height of the adversarial patch according to the size of the attacked aircraft and generates a universal adversarial patch that can attack aircraft of different sizes. In the experiment, we use the YOLOv3 detector to verify the effectiveness of Patch-Noobj on multiple datasets. The experimental results demonstrate that our universal adversarial patches are well adapted to aircraft of different sizes on multiple datasets and effectively reduce the Average Precision (AP) of the YOLOv3 detector on the DOTA, NWPU VHR-10, and RSOD datasets by 48.2%, 23.9%, and 20.2%, respectively. Moreover, the universal adversarial patch generated on one dataset is also effective in attacking aircraft on the remaining two datasets, while the adversarial patch generated on YOLOv3 is also effective in attacking YOLOv5 and Faster R-CNN, which demonstrates the attack transferability of the adversarial patch.

In the age of big data, plenty of valuable sensing data have been shared to enhance scientific innovation. However, this may cause unexpected privacy leakage. Although numerous privacy preservation techniques, such as perturbation, encryption, and anonymization, have been proposed to conceal sensitive information, it is usually at the cost of the application utility. Moreover, most of the existing works did not distinguished the underlying factors, such as data features and sampling rate, which contribute differently to utility and privacy information implied in the shared data. To well balance the application utility and privacy leakage for data sharing, we utilize mutual information and visualization techniques to analyze the impact of the underlying factors on utility and privacy, respectively, and design an interactive visualization tool to help users identify the appropriate solution to achieve the objectives of high application utility and low privacy leakage simultaneously. To illustrate the effectiveness of the proposed scheme and tool, accelerometer data collected from mobile devices have been adopted as an illustrative example. Experimental study has shown that feature selection and sampling frequency play dominant roles in reducing privacy leakage with much less reduction on utility, and the proposed visualization tool can effectively recommend the appropriate combination of features and sampling rates that can help users make decision on the trade-off between utility and privacy.

High-resolution remote sensing images bring a large amount of data as well as challenges to traditional vision tasks. Vehicle re-identification (ReID), as an essential vision task that can utilize remote sensing images, has been widely used in suspect vehicle searches, cross-border vehicle tracking, traffic behavior analysis, and automatic toll collection systems. Although there have been a large number of studies on vehicle ReID, most of them are based on fixed surveillance cameras and do not take full advantage of high-resolution remote sensing images. Compared with images collected by fixed surveillance cameras, high-resolution remote sensing images based on Unmanned Aerial Vehicles (UAVs) have the characteristics of rich viewpoints and a wide range of scale variations. These characteristics bring richer information to vehicle ReID tasks and have the potential to improve the performance of vehicle ReID models. However, to the best of our knowledge, there is a shortage of large open-source datasets for vehicle ReID based on UAV views, which is not conducive to promoting UAV-view-based vehicle ReID research. To address this issue, we construct a large-scale vehicle ReID dataset named VRU (the abbreviation of Vehicle Re-identification based on UAV), which consists of 172,137 images of 15,085 vehicles captured by UAVs, through which each vehicle has multiple images from various viewpoints. Compared with the existing vehicle ReID datasets based on UAVs, the VRU dataset has a larger volume and is fully open-source. Since most of the existing vehicle ReID methods are designed for fixed surveillance cameras, it is difficult for these methods to adapt to UAV-based vehicle ReID images with multi-viewpoint and multi-scale characteristics. Thus, this work proposes a Global Attention and full-Scale Network (GASNet) for the vehicle ReID task based on UAV images. To verify the effectiveness of our GASNet, GASNet is compared with the baseline models on the VRU dataset. The experiment results show that GASNet can achieve 97.45% Rank-1 and 98.51% mAP, which outperforms those baselines by 3.43%/2.08% improvements in terms of Rank-1/mAP. Thus, our major contributions can be summarized as follows: (1) the provision of an open-source UAV-based vehicle ReID dataset, (2) the proposal of a state-of-art model for UAV-based vehicle ReID.

Multi-modal transportation recommendation plays an important role in navigation applications. It aims to recommend a travel plan with various transport modes, such as bus, metro, taxi, bicycle, and a hybrid. Analysis of real-world large-scale navigation data shows that the correlation between the data can be represented by a graph containing different types of nodes and edges. As an emerging technology, graph neural networks (GNN) have shown powerful capabilities in representing graph data. However, existing solutions based on GNN only consider converting heterogeneous graph data into homogeneous graph data, ignoring the effects of different types of nodes and edges. In addition, those methods usually face the over-smoothing problem, which reduces the accuracy of recommendation. To this end, we propose a multi-modal T ransportation recommendation algorithm with H eterogeneous graph A ttention N etworks (THAN) based on carefully constructed heterogeneous graphs. We first design a novel graph embedding method to represent the correlation between the origin and the destination, as well as the correlation between origin-destination (OD) pairs and users. Next, a heterogeneous graph from large-scale data is built to describe the relationship between users, OD pairs, and transport modes. Then, we design a hierarchical attention mechanism with residual blocks to generate node embedding in terms of homogeneity and heterogeneity. Finally, a fusion neural layer is designed to fuse embeddings from different views and predict the proper transport mode for users. Extensive experimental results on a large-scale real-world dataset demonstrate that the performance of THAN outperforms five baselines.