7 papers
Partially Observable Adversarial Patch Attacks on Vision-Language-Action Models in Robotics
Xiaofei Wang, Mingliang Han, Tianyu Hao +3
Vision-language-action (VLA) models are gaining attention in robotics, yet their robustness to adversarial attacks remains largely unexplored. Existing work shows that adversarial…
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 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…
AdvGrasp: Adversarial Attacks on Robotic Grasping from a Physical Perspective
Xiaofei Wang, Mingliang Han, Tianyu Hao +3
Adversarial attacks on robotic grasping provide valuable insights into evaluating and improving the robustness of these systems. Unlike studies that focus solely on neural network…
Cage-Based Deformation for Transferable and Undefendable Point Cloud Attack
Keke Tang, Ziyong Du, Weilong Peng +4
Adversarial attacks on point clouds often impose strict geometric constraints to preserve plausibility; however, such constraints inherently limit transferability and undefendabili…