Chunping Wang

The public sharing of user information opens the door for adversaries to infer private data, leading to privacy breaches and facilitating malicious activities. While numerous studies have concentrated on privacy leakage via public user attributes, the threats associated with the exposure of user relationships, particularly through network structure, are often neglected. This study aims to fill this critical gap by advancing the understanding and protection against privacy risks emanating from network structure, moving beyond direct connections with neighbors to include the broader implications of indirect network structural patterns. To achieve this, we first investigate the problem of Graph Privacy Leakage via Structure (GPS), and introduce a novel measure, the Generalized Homophily Ratio, to quantify the various mechanisms contributing to privacy breach risks in GPS. Based on this insight, we develop a novel graph private attribute inference attack, which acts as a pivotal tool for evaluating the potential for privacy leakage through network structures under worst-case scenarios. To protect users' private data from such vulnerabilities, we propose a graph data publishing method incorporating a learnable graph sampling technique, effectively transforming the original graph into a privacy-preserving version. Extensive experiments demonstrate that our attack model poses a significant threat to user privacy, and our graph data publishing method successfully achieves the optimal privacy-utility trade-off compared to baselines.

Objects in aerial images possess distinctive features such as large-scale variations, intricate backgrounds full of distractions, and versatile viewpoints. These factors present significant challenges for common object detectors based on convolutional networks. The latest transformer-based detector, DEtection TRansformer (DETR), performs impressively. Nevertheless, the attention mechanism's numerous operations limit its application to high-resolution aerial imagery. We have analyzed the design of the DETR-like detector and put forward Decoupled DETR (D2ETR), which aims to process multiscale feature information of aerial images efficiently to balance computational efficiency and accuracy. Our proposal involves an effective decoupled encoder that handles long multiscale feature sequences. The encoder comprises two modules: an attention-based semantic enhancement module and a convolution-based cross-scale fusion module. We have developed a feature stabilization module to counter feature information disorder caused by different processing mechanisms. In addition, we have applied a small-object-friendly loss function to the prediction layer to improve the model's ability to adapt to small targets in aerial images. Experimental results using the VisDrone and DIOR datasets demonstrate our approach reduces computation while maintaining the transformer's excellent performance.

Infrared (IR) passive detection technology is widely utilized for identifying small targets over long distances, owing to its robust anti-interference capabilities and all-weather functionality. As of now, numerous IR small target detection algorithms have been publicly published. To promote research in this field, this paper first briefly introduces single IR small target detection algorithms, which are primarily categorized as model-driven and data-driven. Subsequently, the paper presents human-computer interaction (HCI) systems tailored to these two algorithm categories, incorporating a total of 20 existing model-driven algorithms and 15 data-driven algorithms, respectively. Ultimately, the paper delves into potential avenues for optimizing HCI system design. The proposed IR small target detection HCI systems not only improve user-friendliness, but also provide a benchmark platform for experimental comparison. The introduction of relevant HCI systems can be found on kourenke/GUI-system-for-IR-small-target-detection (github.com).

Camouflaged object detection (COD) aims to identify objects that are perfectly concealed in their surroundings and has attracted increasing attention in recent years. The challenge with COD is the intrinsic similarity between camouflaged objects and background, as well as the weak boundary that often accompanies camouflaged objects. In this paper, a Progressive Refinement Network called PRNet is proposed based on human perception of camouflaged images. Specifically, we develop a position-aware module to roughly locate the position of camouflaged objects by reverse-guiding with high-level semantic information. Moreover, an edge-guided fusion module is designed to simultaneously refine the boundaries and regions of camouflaged objects by using edge features as a guide in cross-level feature fusion. Benefited from the utility of the above two modules, our PRNet is able to identify camouflaged objects accurately and quickly. Numerous experiments on four widely used benchmark datasets demonstrate that the proposed PRNet is an efficient COD model, outperforming 14 state-of-the-art algorithms significantly and running at a real-time

In recent years, graph pre-training has gained significant attention, focusing on acquiring transferable knowledge from unlabeled graph data to improve downstream performance. Despite these recent endeavors, the problem of negative transfer remains a major concern when utilizing graph pre-trained models to downstream tasks. Previous studies made great efforts on the issue of what to pre-train and how to pre-train by designing a variety of graph pre-training and fine-tuning strategies. However, there are cases where even the most advanced "pre-train and fine-tune" paradigms fail to yield distinct benefits. This paper introduces a generic framework W2PGNN to answer the crucial question of when to pre-train (.e., in what situations could we take advantage of graph pre-training) before performing effortful pre-training or fine-tuning. We start from a new perspective to explore the complex generative mechanisms from the pre-training data to downstream data. In particular, W2PGNN first fits the pre-training data into graphon bases, each element of graphon basis (i.e., a graphon) identifies a fundamental transferable pattern shared by a collection of pre-training graphs. All convex combinations of graphon bases give rise to a generator space, from which graphs generated form the solution space for those downstream data that can benefit from pre-training. In this manner, the feasibility of pre-training can be quantified as the generation probability of the downstream data from any generator in the generator space. W2PGNN offers three broad applications: providing the application scope of graph pre-trained models, quantifying the feasibility of pre-training, and assistance in selecting pre-training data to enhance downstream performance. We provide a theoretically sound solution for the first application and extensive empirical justifications for the latter two applications.

Ship detection is a significant and challenging task in remote sensing. At present, anchor-based ship detectors have achieved remarkable results, but they require introducing additional parameters and their performance is easily affected by the size of anchor boxes. In this paper, we propose an anchor-free rotated detector (OFCOS) for ship detection based on FCOS, which can be trained end-to-end. Specifically, a feature-enhanced feature pyramid network (FE-FPN) is proposed, in which the structure of the feature pyramid is optimized and an attention mechanism is introduced during fusion to enhance the significance of object features. Then, to better describe the orientation of objects, a regression branch with orientation characterization capability is constructed, and a center-to-corner bounding box prediction strategy is used to improve the accuracy of object localization. Moreover, the calculation of center-ness is optimized so that the assignment of center weights is orientation-aware and adaptive to down-weight low-quality predictions. A new remote sensing ship dataset, named RS-Ship, is constructed to further verify the effectiveness and robustness of OFCOS. Our experiments show that OFCOS achieves AP values of 91.07% and 97.05% on the publicly available dataset HRSC2016 and our self-built RS-Ship dataset, respectively, which are 13.01% and 9.84% higher than FCOS. OFCOS outperforms other mainstream detection methods in terms of both detection speed and detection accuracy.

With the recent development of deep convolutional neural network (CNN), remote sensing for ship detection methods has achieved enormous progress. However, current methods focus on the whole ships and fail on the components detection of a ship. To detect ships from remote-sensing images in a more refined way, we employ the inherent relationship between ships and their critical parts to establish a multilevel structure and propose a novel framework to improve the performance in identifying the multilevel objects. Our framework, named the dual detector network (DD-Net), consists of two carefully designed detectors, one for ships (the ship detector) and the other for their critical parts (the critical part detector), for detecting the critical parts in a coarse-to-fine manner. The ship detector offers detection results of the ship, based on which the critical part detector detects small critical parts inside each ship region. The framework is trained in an end-to-end way by optimizing the multitask loss. Due to the lack of publicly available datasets for critical part detection, we build a new dataset named RS-Ship with 1015 remote-sensing images and 2856 annotations. Experiments on the HRSC2016 dataset and the RS-Ship dataset show that our method performs well in the detection of ships and critical parts.

Ship detection is one of the fundamental tasks in computer vision. In recent years, the methods based on convolutional neural networks have made great progress. However, improvement of ship detection in aerial images is limited by large-scale variation, aspect ratio, and dense distribution. In this paper, a Critical and Align Feature Constructing Network (CAFC-Net) which is an end-to-end single-stage rotation detector is proposed to improve ship detection accuracy. The framework is formed by three modules: a Biased Attention Module (BAM), a Feature Alignment Module (FAM), and a Distinctive Detection Module (DDM). Specifically, the BAM extracts biased critical features for classification and regression. With the extracted biased regression features, the FAM generates high-quality anchor boxes. Through a novel Alignment Convolution, convolutional features can be aligned according to anchor boxes. The DDM produces orientation-sensitive feature and reconstructs orientation-invariant features to alleviate inconsistency between classification and localization accuracy. Extensive experiments on two remote sensing datasets HRS2016 and self-built ship datasets show the state-of-the-art performance of our detector.

With the development of big data and algorithms, deep convolution neural networks with more hidden layers have more powerful feature learning and feature expression ability than traditional machine learning methods, making artificial intelligence surpass human level in many fields. This paper first reviews the development and application of deep convolutional neural networks in the field of object detection in recent years, then briefly summarizes and ponders some existing problems in the current research, and the future development of deep convolutional neural network is prospected.