12 papers
Rethinking Transferable Adversarial Attacks on Point Clouds from a Compact Subspace Perspective
Keke Tang, Xianheng Liu, Weilong Peng +5
Transferable adversarial attacks on point clouds remain challenging, as existing methods often rely on model-specific gradients or heuristics that limit generalization to unseen ar…
Optimal Transport-Induced Samples against Out-of-Distribution Overconfidence
Keke Tang, Ziyong Du, Xiaofei Wang +3
Deep neural networks (DNNs) often produce overconfident predictions on out-of-distribution (OOD) inputs, undermining their reliability in open-world environments. Singularities in…
Transferable Hypergraph Attack via Injecting Nodes into Pivotal Hyperedges
Meixia He, Peican Zhu, Le Cheng +3
Recent studies have demonstrated that hypergraph neural networks (HGNNs) are susceptible to adversarial attacks. However, existing methods rely on the specific information mechanis…
Transferable and Undefendable Point Cloud Attacks via Medial Axis Transform
Keke Tang, Yuze Gao, Weilong Peng +3
Studying adversarial attacks on point clouds is essential for evaluating and improving the robustness of 3D deep learning models. However, most existing attack methods are develope…
SEER: Semantic Enhancement and Emotional Reasoning Network for Multimodal Fake News Detection
Peican Zhu, Yubo Jing, Le Cheng +4
Previous studies on multimodal fake news detection mainly focus on the alignment and integration of cross-modal features, as well as the application of text-image consistency. Howe…
KEN: Knowledge Augmentation and Emotion Guidance Network for Multimodal Fake News Detection
Peican Zhu, Yubo Jing, Le Cheng +2
In recent years, the rampant spread of misinformation on social media has made accurate detection of multimodal fake news a critical research focus. However, previous research has…