1 citations · 2 across the 11 of their papers we have counts for
12 papers
Mean Masked Autoencoder with Flow-Mixing for Encrypted Traffic Classification
Xiao Liu, Xiaowei Fu, Fuxiang Huang +1
Network traffic classification using self-supervised pre-training models based on Masked Autoencoders (MAE) has demonstrated a huge potential. However, existing methods are confine…
TrafficMoE: Heterogeneity-aware Mixture of Experts for Encrypted Traffic Classification
Qing He, Xiaowei Fu, Lei Zhang
Encrypted traffic classification is a critical task for network security. While deep learning has advanced this field, the occlusion of payload semantics by encryption severely cha…
Proxy Robustness in Vision Language Models is Effortlessly Transferable
Xiaowei Fu, Fuxiang Huang, Lei Zhang
As a pivotal technique for improving the defense of deep models, adversarial robustness transfer via distillation has demonstrated remarkable success in conventional image classifi…
Adversarial Defense in Vision-Language Models: An Overview
Xiaowei Fu, Lei Zhang
The widespread use of Vision Language Models (VLMs, e.g. CLIP) has raised concerns about their vulnerability to sophisticated and imperceptible adversarial attacks. These attacks c…
Unsupervised Robust Domain Adaptation: Paradigm, Theory and Algorithm
Fuxiang Huang, Xiaowei Fu, Shiyu Ye +5
Unsupervised domain adaptation (UDA) aims to transfer knowledge from a label-rich source domain to an unlabeled target domain by addressing domain shifts. Most UDA approaches empha…
Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense
Lina Ma, Xiaowei Fu, Fuxiang Huang +2
Existing defense methods fail to defend against unknown attacks and thus raise generalization issue of adversarial robustness. To remedy this problem, we attempt to delve into some…
A Survey of Multimodal Composite Editing and Retrieval
Suyan Li, Fuxiang Huang, Lei Zhang
In the real world, where information is abundant and diverse across different modalities, understanding and utilizing various data types to improve retrieval systems is a key focus…
Meta Invariance Defense Towards Generalizable Robustness to Unknown Adversarial Attacks
Lei Zhang, Yuhang Zhou, Yi Yang +1
Despite providing high-performance solutions for computer vision tasks, the deep neural network (DNN) model has been proved to be extremely vulnerable to adversarial attacks. Curre…
An Open-World, Diverse, Cross-Spatial-Temporal Benchmark for Dynamic Wild Person Re-Identification
Lei Zhang, Xiaowei Fu, Fuxiang Huang +2
Person re-identification (ReID) has made great strides thanks to the data-driven deep learning techniques. However, the existing benchmark datasets lack diversity, and models train…
Invariance-powered Trustworthy Defense via Remove Then Restore
Xiaowei Fu, Yuhang Zhou, Lina Ma +1
Adversarial attacks pose a challenge to the deployment of deep neural networks (DNNs), while previous defense models overlook the generalization to various attacks. Inspired by tar…
Dynamic Weighted Combiner for Mixed-Modal Image Retrieval
Fuxiang Huang, Lei Zhang, Xiaowei Fu +1
Mixed-Modal Image Retrieval (MMIR) as a flexible search paradigm has attracted wide attention. However, previous approaches always achieve limited performance, due to two critical…
On the Robustness of Domain Adaption to Adversarial Attacks
Liyuan Zhang, Yuhang Zhou, Lei Zhang
State-of-the-art deep neural networks (DNNs) have been proved to have excellent performance on unsupervised domain adaption (UDA). However, recent work shows that DNNs perform poor…