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Le Cheng

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7 papers

1 author row

AAAI Conference 2026 Conference Paper

Transferable Hypergraph Attack via Injecting Nodes into Pivotal Hyperedges

Meixia He
Peican Zhu
Le Cheng
Yangming Guo
Manman Yuan
Keke Tang

Recent studies have demonstrated that hypergraph neural networks (HGNNs) are susceptible to adversarial attacks. However, existing methods rely on the specific information mechanisms of target HGNNs, overlooking the common vulnerability caused by the significant differences in hyperedge pivotality along aggregation paths in most HGNNs, thereby limiting the transferability and effectiveness of attacks. In this paper, we present a novel framework, i.e., Transferable Hypergraph Attack via Injecting Nodes into Pivotal Hyperedges (TH-Attack), to address these limitations. Specifically, we design a hyperedge recognizer via pivotality assessment to obtain pivotal hyperedges within the aggregation paths of HGNNs. Furthermore, we introduce a feature inverter based on pivotal hyperedges, which generates malicious nodes by maximizing the semantic divergence between the generated features and the pivotal hyperedges features. Lastly, by injecting these malicious nodes into the pivotal hyperedges, TH-Attack improves the transferability and effectiveness of attacks. Extensive experiments are conducted on six authentic datasets to validate the effectiveness of TH-Attack and the corresponding superiority to state-of-the-art methods.

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IJCAI Conference 2025 Conference Paper

HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion

Le Cheng
Peican Zhu
Yangming Guo
Keke Tang
Chao Gao
Zhen Wang

Hypergraphs offer superior modeling capabilities for social networks, particularly in capturing group phenomena that extend beyond pairwise interactions in rumor propagation. Existing approaches in rumor source detection predominantly focus on dyadic interactions, which inadequately address the complexity of more intricate relational structures. In this study, we present a novel approach for Source Detection in Hypergraphs (HyperDet) via Interactive Relationship Construction and Feature-rich Attention Fusion. Specifically, our methodology employs an Interactive Relationship Construction module to accurately model both the static topology and dynamic interactions among users, followed by the Feature-rich Attention Fusion module, which autonomously learns node features and discriminates between nodes using a self-attention mechanism, thereby effectively learning node representations under the framework of accurately modeled higher-order relationships. Extensive experimental validation confirms the efficacy of our HyperDet approach, showcasing its superiority relative to current state-of-the-art methods.

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IJCAI Conference 2025 Conference Paper

SourceDetMamba: A Graph-aware State Space Model for Source Detection in Sequential Hypergraphs

Le Cheng
Peican Zhu
Yangming Guo
Chao Gao
Zhen Wang
Keke Tang

Source detection on graphs has demonstrated high efficacy in identifying rumor origins. Despite advances in machine learning-based methods, many fail to capture intrinsic dynamics of rumor propagation. In this work, we present SourceDetMamba: A Graph-aware State Space Model for Source Detection in Sequential Hypergraphs, which harnesses the recent success of the state space model Mamba, known for its superior global modeling capabilities and computational efficiency, to address this challenge. Specifically, we first employ hypergraphs to model high-order interactions within social networks. Subsequently, temporal network snapshots generated during the propagation process are sequentially fed in reverse order into Mamba to infer underlying propagation dynamics. Finally, to empower the sequential model to effectively capture propagation patterns while integrating structural information, we propose a novel graph-aware state update mechanism, wherein the state of each node is propagated and refined by both temporal dependencies and topological context. Extensive evaluations on eight datasets demonstrate that SourceDetMamba consistently outperforms state-of-the-art approaches.

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EAAI Journal 2024 Journal Article

Efficient segmentation with texture in ore images based on box-supervised approach

Guodong Sun
Delong Huang
Yuting Peng
Le Cheng
Bo Wu
Yang Zhang

Image segmentation methods have been utilized to determine the particle size distribution of crushed ores. Due to the complex working environment, high-powered computing equipment is difficult to deploy. At the same time, the ore distribution is stacked, and it is difficult to identify the complete features. To address this issue, an effective box-supervised technique with texture features is provided for ore image segmentation that can identify complete and independent ores. Firstly, a ghost feature pyramid network (Ghost-FPN) is proposed to process the features obtained from the backbone to reduce redundant semantic information and computation generated by complex networks. Then, an optimized detection head is proposed to obtain the feature to maintain accuracy. Finally, Lab color space (Lab) and local binary patterns (LBP) texture features are combined to form a fusion feature similarity-based loss function to improve accuracy while incurring no loss. Experiments on MS COCO have shown that the proposed fusion features are also worth studying on other types of datasets. Extensive experimental results demonstrate the effectiveness of the proposed method, which achieves over 50 frames per second with a small model size of 21. 6 MB. Meanwhile, the method maintains a high level of accuracy 67. 8 in A P 50 b o x and 47. 7 in A P 50 m a s k compared with the state-of-the-art approaches on ore image dataset, even better than bounding box tightness prior (BBTP) by 10. 4/1. 3 on A P 50 b o x / A P 50 m a s k metrics with the ResNet50 as backbone. The source code is available at https: //github. com/MVME-HBUT/OREINST.

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EAAI Journal 2024 Journal Article

FS-OreDet: Feature enhancement and relationship exploration for boosting few-shot object detector of ore images

Guodong Sun
Le Cheng
Jinyu Liu
Yuting Peng
Chengming Xu
Yanwei Fu
Bo Wu
Yang Zhang

In the ore beneficiation process, large block detection is necessary to ensure production safety. This typically involves identifying oversized ore on the conveyor belt and preventing material blockage accidents in the transfer buffer bin between the ore feeding belt and the ore receiving belt. Methods based on deep learning can learn to construct complex features from a large amount of data, but they also require a large number of hand-made datasets for training. Although the existing few shot detection methods for ore images reduce the cost of manual labeling, the corresponding detection performance is insufficient. This article mainly explores how to improve the performance of the detector under the ore image detection task in the case of few labeled images. First, a shot enhancement block is proposed to enhance the valuable foreground information for higher-quality support features. Subsequently, we present a dual-attention region proposal network that effectively leverages support features to enhance the precision of generating candidate proposals. Finally, we propose a lightweight multi-relational detector to effectively evaluate the relationship between query and support proposals, leading to a substantial enhancement in guidance performance. The proposed few-shot object detector (FS-OreDet) achieves the best detection results with state-of-the-art methods with an average precision ( A P ) of 55. 1, a speed of 57 frames per second ( F P S ), and a model size of only 17 M B. Furthermore, our framework adeptly captures the feature information of ore images with substantial data. The detector’s accuracy achieves a significant improvement of 14% in A P. Compared with general object detectors, the performance of the detector ranks first and meets the requirements for outdoor scene deployment.

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AAAI Conference 2024 Conference Paper

GIN-SD: Source Detection in Graphs with Incomplete Nodes via Positional Encoding and Attentive Fusion

Le Cheng
Peican Zhu
Keke Tang
Chao Gao
Zhen Wang

Source detection in graphs has demonstrated robust efficacy in the domain of rumor source identification. Although recent solutions have enhanced performance by leveraging deep neural networks, they often require complete user data. In this paper, we address a more challenging task, rumor source detection with incomplete user data, and propose a novel framework, i.e., Source Detection in Graphs with Incomplete Nodes via Positional Encoding and Attentive Fusion (GIN-SD), to tackle this challenge. Specifically, our approach utilizes a positional embedding module to distinguish nodes that are incomplete and employs a self-attention mechanism to focus on nodes with greater information transmission capacity. To mitigate the prediction bias caused by the significant disparity between the numbers of source and non-source nodes, we also introduce a class-balancing mechanism. Extensive experiments validate the effectiveness of GIN-SD and its superiority to state-of-the-art methods.

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YNIMG Journal 2018 Journal Article

Detectability and reproducibility of the olfactory fMRI signal under the influence of magnetic susceptibility artifacts in the primary olfactory cortex

Jiaming Lu
Xin Wang
Zhao Qing
Zhu Li
Wen Zhang
Ying Liu
Lihua Yuan
Le Cheng

For human olfactory functional MRI studies, the primary olfactory cortex (POC) suffers severe magnetic susceptibility artifacts, which adversely influences the detectability and reproducibility of the olfactory fMRI data and its clinical applications. The goal of this work is to assess the impacts of the image artifacts on the detectability and reproducibility of the olfactory activation in the POC. The severity of artifacts in the POC were classified into three levels using a Subjective Artifact score (SA_score). The mean temporal signal-to-noise ratio (tSNR) of the fMRI data acquired by a given MRI sequence and olfactory activation (β value) in POC were evaluated and compared to the concurrent activations in the primary visual cortex (Brodmann area 17, BA17) by an odor-visual association paradigm using ninety-nine normal human subjects. Our study revealed that the mean tSNR in POC was above the threshold for reliable detection of the functional activation signal, and, consequently, the mean olfactory activations in the POC were not significantly different from those in BA17. The reproducibility of the activation in the POC was assessed by a random half-split stimulation of a test-retest experiment. The overlap of the activation maps for all the trials (n = 1000) in the POC were not statistically different from that observed in BA17. These results show that the detectability and reproducibility of olfactory activation in the presence of susceptibility artifacts in the POC was at similar level of that in the visual cortex.

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