Rui Fan

Ji-Ping Jin, Chen-Bin Feng, Rui Fan et al.

Image stitching often faces challenges due to varying capture angles, positional differences, and object movements, leading to misalignments and visual discrepancies. Traditional seam carving methods neglect semantic information, causing disruptions in foreground continuity. We introduce SemanticStitch, a deep learning-based framework that incorporates semantic priors of foreground objects to preserve their integrity and enhance visual coherence. Our approach includes a novel loss function that emphasizes the semantic integrity of salient objects, significantly improving stitching quality. We also present two specialized real-world datasets to evaluate our method's effectiveness. Experimental results demonstrate substantial improvements over traditional techniques, providing robust support for practical applications.

Zhihao Zhu, Rui Fan, Chenwang Wu et al.

Recent studies have demonstrated the vulnerability of recommender systems to data privacy attacks. However, research on the threat to model privacy in recommender systems, such as model stealing attacks, is still in its infancy. Some adversarial attacks have achieved model stealing attacks against recommender systems, to some extent, by collecting abundant training data of the target model (target data) or making a mass of queries. In this paper, we constrain the volume of available target data and queries and utilize auxiliary data, which shares the item set with the target data, to promote model stealing attacks. Although the target model treats target and auxiliary data differently, their similar behavior patterns allow them to be fused using an attention mechanism to assist attacks. Besides, we design stealing functions to effectively extract the recommendation list obtained by querying the target model. Experimental results show that the proposed methods are applicable to most recommender systems and various scenarios and exhibit excellent attack performance on multiple datasets.

Yikang Zhang, Chuang-Wei Liu, Jiahang Li et al.

Road inspection is essential for ensuring road maintenance and traffic safety, as road defects gradually emerge and compromise road functionality. Traditional methods, which rely on manual evaluations, are labor-intensive, costly, and time-consuming. Although data-driven approaches are gaining traction, the scarcity and spatial sparsity of road defects in the real world pose significant challenges in acquiring high-quality datasets. Existing simulators designed to generate detailed synthetic driving scenes, however, lack models for road defects. Furthermore, advanced driving tasks involving interactions with road surfaces, such as planning and control in defective areas, remain underexplored. To address these limitations, we propose a system based on Urban Digital Twin (UDT) technology for intelligent road inspection. First, hierarchical road models are constructed from real-world driving data, creating highly detailed representations of road defect structures and surface elevations. Next, digital road twins are generated to create simulation environments for comprehensive analysis and evaluation. These scenarios are subsequently imported into a simulator to enable both data acquisition and physical simulation. Experimental results demonstrate that driving tasks, including perception and decision-making, can be significantly improved using the high-fidelity road defect scenes generated by our system.