4 citations · 7 across the 4 of their papers we have counts for
4 papers
When Robots Obey the Patch: Universal Transferable Patch Attacks on Vision-Language-Action Models
Hui Lu, Yi Yu, Yiming Yang +5
Vision-Language-Action (VLA) models are vulnerable to adversarial attacks, yet universal and transferable attacks remain underexplored, as most existing patches overfit to a single…
Semantic Deep Hiding for Robust Unlearnable Examples
Ruohan Meng, Chenyu Yi, Yi Yu +3
Ensuring data privacy and protection has become paramount in the era of deep learning. Unlearnable examples are proposed to mislead the deep learning models and prevent data from u…
Temporal Coherent Test-Time Optimization for Robust Video Classification
Chenyu Yi, Siyuan Yang, Yufei Wang +3
Deep neural networks are likely to fail when the test data is corrupted in real-world deployment (e.g., blur, weather, etc.). Test-time optimization is an effective way that adapts…
Benchmarking the Robustness of Spatial-Temporal Models Against Corruptions
Chenyu Yi, Siyuan Yang, Haoliang Li +2
The state-of-the-art deep neural networks are vulnerable to common corruptions (e.g., input data degradations, distortions, and disturbances caused by weather changes, system error…