Wei Ge

Junhuan Liu, San Jiang, Wei Ge et al.

The primary contribution of this paper is a challenging benchmark dataset, UAVPairs, and a training pipeline designed for match pair retrieval of large-scale UAV images. First, the UAVPairs dataset, comprising 21,622 high-resolution images across 30 diverse scenes, is constructed; the 3D points and tracks generated by SfM-based 3D reconstruction are employed to define the geometric similarity of image pairs, ensuring genuinely matchable image pairs are used for training. Second, to solve the problem of expensive mining cost for global hard negative mining, a batched nontrivial sample mining strategy is proposed, leveraging the geometric similarity and multi-scene structure of the UAVPairs to generate training samples as to accelerate training. Third, recognizing the limitation of pair-based losses, the ranked list loss is designed to improve the discrimination of image retrieval models, which optimizes the global similarity structure constructed from the positive set and negative set. Finally, the effectiveness of the UAVPairs dataset and training pipeline is validated through comprehensive experiments on three distinct large-scale UAV datasets. The experiment results demonstrate that models trained with the UAVPairs dataset and the ranked list loss achieve significantly improved retrieval accuracy compared to models trained on existing datasets or with conventional losses. Furthermore, these improvements translate to enhanced view graph connectivity and higher quality of reconstructed 3D models. The models trained by the proposed approach perform more robustly compared with hand-crafted global features, particularly in challenging repetitively textured scenes and weakly textured scenes. For match pair retrieval of large-scale UAV images, the trained image retrieval models offer an effective solution. The dataset would be made publicly available at https://github.com/json87/UAVPairs.

Zhaopeng Gu, Bingke Zhu, Guibo Zhu et al.

Anomaly detection is a critical task across numerous domains and modalities, yet existing methods are often highly specialized, limiting their generalizability. These specialized models, tailored for specific anomaly types like textural defects or logical errors, typically exhibit limited performance when deployed outside their designated contexts. To overcome this limitation, we propose AnomalyMoE, a novel and universal anomaly detection framework based on a Mixture-of-Experts (MoE) architecture. Our key insight is to decompose the complex anomaly detection problem into three distinct semantic hierarchies: local structural anomalies, component-level semantic anomalies, and global logical anomalies. AnomalyMoE correspondingly employs three dedicated expert networks at the patch, component, and global levels, and is specialized in reconstructing features and identifying deviations at its designated semantic level. This hierarchical design allows a single model to concurrently understand and detect a wide spectrum of anomalies. Furthermore, we introduce an Expert Information Repulsion (EIR) module to promote expert diversity and an Expert Selection Balancing (ESB) module to ensure the comprehensive utilization of all experts. Experiments on 8 challenging datasets spanning industrial imaging, 3D point clouds, medical imaging, video surveillance, and logical anomaly detection demonstrate that AnomalyMoE establishes new state-of-the-art performance, significantly outperforming specialized methods in their respective domains.

Zhuotao Liu, Yuan Cao, Min Zhu et al.

Defending against distributed denial of service (DDoS) attacks in the Internet is a fundamental problem. However, recent industrial interviews with over 100 security experts from more than ten industry segments indicate that DDoS problems have not been fully addressed. The reasons are twofold. On one hand, many academic proposals that are provably secure witness little real-world deployment. On the other hand, the operation model for existing DDoS-prevention service providers (e.g., Cloudflare, Akamai) is privacy invasive for large organizations (e.g., government). In this paper, we present Umbrella, a new DDoS defense mechanism enabling Internet Service Providers (ISPs) to offer readily deployable and privacy-preserving DDoS prevention services to their customers. At its core, Umbrella develops a multi-layered defense architecture to defend against a wide spectrum of DDoS attacks. In particular, the flood throttling layer stops amplification-based DDoS attacks; the congestion resolving layer, aiming to prevent sophisticated attacks that cannot be easily filtered, enforces congestion accountability to ensure that legitimate flows are guaranteed to receive their fair shares regardless of attackers' strategies; and finally the userspecific layer allows DDoS victims to enforce self-desired traffic control policies that best satisfy their business requirements. Based on Linux implementation, we demonstrate that Umbrella is capable to deal with large scale attacks involving millions of attack flows, meanwhile imposing negligible packet processing overhead. Further, our physical testbed experiments and large scale simulations prove that Umbrella is effective to mitigate various DDoS attacks.