1 citations · 1 across the 3 of their papers we have counts for
4 papers
Enhancing Adversarial Transferability in Visual-Language Pre-training Models via Local Shuffle and Sample-based Attack
Xin Liu, Aoyang Zhou
Visual-Language Pre-training (VLP) models have achieved significant performance across various downstream tasks. However, they remain vulnerable to adversarial examples. While prio…
Revisiting Edge Perturbation for Graph Neural Network in Graph Data Augmentation and Attack
Xin Liu, Yuxiang Zhang, Meng Wu +6
Edge perturbation is a basic method to modify graph structures. It can be categorized into two veins based on their effects on the performance of graph neural networks (GNNs), i.e.…
Fast Adversarial Training against Textual Adversarial Attacks
Yichen Yang, Xin Liu, Kun He
Many adversarial defense methods have been proposed to enhance the adversarial robustness of natural language processing models. However, most of them introduce additional pre-set…
AutoAugment Input Transformation for Highly Transferable Targeted Attacks
Haobo Lu, Xin Liu, Kun He
Deep Neural Networks (DNNs) are widely acknowledged to be susceptible to adversarial examples, wherein imperceptible perturbations are added to clean examples through diverse input…