Graph Neural Networks (GNNs) have achieved notable success in a wide range of applications. However, their vulnerability to adversarial attacks, particularly graph injection attacks (GIAs), raises serious concerns for their deployment in security-sensitive domains. Existing GIA methods, despite their demonstrated effectiveness, face several inherent limitations. They typically require training surrogate models to approximate the victim model's behavior, which may lead to performance degradation when the surrogate mismatches the target model. Furthermore, the discrete nature of graph data poses challenges for generating effective adversarial features, often resulting in suboptimal solutions. Most critically, these methods show markedly reduced effectiveness when deployed against defended GNN models, limiting real-world applicability. To address these challenges, we introduce SoftHist, a novel gradient-free reinforcement learning framework for black-box graph injection attacks. Our approach incorporates a softened embedding mechanism to avoid suboptimal feature generation, ensuring stable and stealthy node injection. Moreover, we design a topology-aware edge sampler and a defense-aware policy learner with adaptive history reuse optimized for misclassification maximization. These innovations collectively balance attack effectiveness, stealthiness, and robustness against defensive measures. Extensive experiments on eight benchmark datasets demonstrate SoftHist's significant advantages in key scenarios: (1) On discrete-feature datasets like AMComputer, the misclassification rate is 10.34%~38.09% higher than baseline methods; (2) Against defensive models such as RGCN, it maintains 98.23% success rate, surpassing state-of-the-art methods by 12.36%.

Spoofing detection in financial trading is crucial, especially for identifying complex behaviors such as conspiracy spoofing. Traditional machine-learning approaches primarily focus on isolated node features, often overlooking the broader context of interconnected nodes. Graph-based techniques, particularly Graph Neural Networks (GNNs), have advanced the field by leveraging relational information effectively. However, in real-world spoofing detection datasets, trading behaviors exhibit dynamic, irregular patterns. Existing spoofing detection methods, though effective in some scenarios, struggle to capture the complexity of dynamic and diverse, evolving inter-node relationships. To address these challenges, we propose a novel framework called the Generative Dynamic Graph Model (GDGM), which models dynamic trading behaviors and the relationships among nodes to learn representations for conspiracy spoofing detection. Specifically, our approach incorporates the generative dynamic latent space to capture the temporal patterns and evolving market conditions. Raw trading data is first converted into time-stamped sequences. Then we model trading behaviors using the neural ordinary differential equations and gated recurrent units, to generate the representation incorporating temporal dynamics of spoofing patterns. Furthermore, pseudo-label generation and heterogeneous aggregation techniques are employed to gather relevant information and enhance the detection performance for conspiratorial spoofing behaviors. Experiments conducted on spoofing detection datasets demonstrate that our approach outperforms state-of-the-art models in detection accuracy. Additionally, our spoofing detection system has been successfully deployed in one of the largest global trading markets, further validating the practical applicability and performance of the proposed method.

In this paper, we explore the impact of the spatial properties of point clouds on 3D object detection in autonomous driving scenarios. To reduce the memory and computational costs, existing point-based models typically use random sampling or the farthest point sampling strategy to retain the foreground points or points closer to the center of the object. However, they treated points with different spatial properties equally, which led to the loss of potential relative geometric information in the point cloud. To this end, we design a two-stage detector that fully exploits the spatial properties of point clouds, termed Dual Position Aware 3D Object Detector(DPA-RCNN). Specifically, we first sample more points close to the object center by exploiting the relative position information between points and the object center. These sampling points can generate more precisely positioned proposals, which can reduce the difficulty of subsequent bounding box regression stages. In addition, we use the distance from the point to the edge of the bounding box to learn edge features in proposals, which further improves the regression accuracy of the bounding box. Experiments on KITTI and ONCE datasets validate the superiority and universality of our approach. Our method outperforms all point-based detectors, and the proposed Edge Point-aware Segmentation Module(EPAS) can effectively improve the detection accuracy of two-stage detectors.