Author

Liwen Wu

View on Semantic Scholar

2 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Preprint Aug 2026

ProTAGAD: A Foundation Model for TAG Anomaly Detection with Decoupled Topological and Textual Prototypes

Text-Attributed Graphs (TAGs), endowed with abundant textual content along with topological structures, have emerged as a versatile backbone for real-world anomaly detection spanning large language model security, social network moderation, and cyber threat identification. Unlike conventional Graph Anomaly Detection (GAD), which relies primarily on structural irregularities, TAG anomaly detection must jointly leverage both topological patterns and fine-grained textual semantics to capture nuanced anomalous behaviors. The current GNN-based anomaly detectors adopt holistic message-passing schemes that indiscriminately fuse structural proximity and textual semantics during propagation, leading to deep cross-modality coupling. This entanglement acts as a noise amplifier, obscuring subtle anomalous signals and directly giving rise to the Blurred-Anomaly-Boundary (BAB) issue by rendering normal-anomalous decision boundaries poorly separable. This challenge is further amplified for graph foundation models that require robust cross-domain generalization. To bridge this gap, we introduce a novel foundation model for TAG anomaly detection featuring decoupled topological and textual prototypes. Our framework constructs dual prototype banks to independently model structural normality and semantic consistency, effectively isolating anomaly cues that are otherwise diluted during coupled aggregation. Extensive experiments across 14 diverse benchmark datasets demonstrate that our method consistently achieves state-of-the-art performance in cross-domain settings. Notably, the ablation studies further corroborate the prevalence of the BAB issue in conventional coupled TAG anomaly detectors, and show that our decoupled prototype design effectively mitigates this challenge.

Ziyang Wang, Liwen Wu, Cheng Xie et al. · 0 citations

Open access Jul 2026

Boosting the Transferability of Adversarial Attacks with Semantic-Invariance and Low-Gradient Replacement

Deepneural networks are highly vulnerable to adversarial examples, which are generatedby introducing subtle perturbations to input data to mislead model classification. Currently,transfer-based attacks are prevalent in adversarial example generation and canbe categorized into input transformation-based and gradient-based methods. However,most input transformation-based methods tend to produce augmented replicas that aresemantically inconsistent with the original inputs, while gradient-based methods oftenleave low-gradient regions unperturbed within the model’s critical attention areas. Theselimitations constrain further improvements in adversarial transferability. In this work, wepropose a Semantic-Invariance and Low-Gradient Replacement Method (SLRM) to addressthese challenges. Our framework integrates semantically consistent augmentation andgradient replacement as follows: (1) a feature extractor captures semantic features fromoriginal inputs and a reconstructor generates augmented replicas that preserve semanticfidelity to enhance input diversity, and (2) low-gradient regions in adversarial examplesare systematically replaced with corresponding regions from augmented replicas to eliminatethe under-perturbed areas critical for model robustness. Comprehensive empiricalevaluations on ImageNet demonstrate that SLRM significantly enhances the transferabilityof baseline methods and seamlessly integrates with state-of-the-art approaches to furtherimprove their performance. Moreover, SLRM substantially improves the robustness ofbaseline methods against defended models, achieving superior attack success rates underadvanced adversarial defenses.

Yunqi Zhang, Wei Chen, Lei Zhao et al. · Mathematics · 0 citations