3 citations · 3 across the 2 of their papers we have counts for
4 papers
MAPE: Defending Against Transferable Adversarial Attacks Using Multi-Source Adversarial Perturbations Elimination
Xinlei Liu, Jichao Xie, Tao Hu +4
Neural networks are vulnerable to meticulously crafted adversarial examples, leading to high-confidence misclassifications in image classification tasks. Due to their consistency w…
SDM: A Powerful Tool for Evaluating Model Robustness
Xinlei Liu, Tao Hu, Jichao Xie +3
Gradient-based attacks are important methods for evaluating model robustness. However, since the proposal of APGD, it has been difficult for such methods to achieve significant bre…
Emergent Dexterity via Diverse Resets and Large-Scale Reinforcement Learning
Patrick Yin, Tyler Westenbroek, Zhengyu Zhang +9
Reinforcement learning in massively parallel physics simulations has driven major progress in sim-to-real robot learning. However, current approaches remain brittle and task-specif…
Sequential Difference Maximization: Generating Adversarial Examples via Multi-Stage Optimization
Xinlei Liu, Tao Hu, Peng Yi +3
Efficient adversarial attack methods are critical for assessing the robustness of computer vision models. In this paper, we reconstruct the optimization objective for generating ad…