26 citations · 68 across the 10 of their papers we have counts for
10 papers
Securely Fine-tuning Pre-trained Encoders Against Adversarial Examples
Ziqi Zhou, Minghui Li, Wei Liu +7
With the evolution of self-supervised learning, the pre-training paradigm has emerged as a predominant solution within the deep learning landscape. Model providers furnish pre-trai…
MISA: Unveiling the Vulnerabilities in Split Federated Learning
Wei Wan, Yuxuan Ning, Shengshan Hu +4
\textit{Federated learning} (FL) and \textit{split learning} (SL) are prevailing distributed paradigms in recent years. They both enable shared global model training while keeping…
AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal Contrastive Learning
Ziqi Zhou, Shengshan Hu, Minghui Li +3
Multimodal contrastive learning aims to train a general-purpose feature extractor, such as CLIP, on vast amounts of raw, unlabeled paired image-text data. This can greatly benefit…
A Four-Pronged Defense Against Byzantine Attacks in Federated Learning
Wei Wan, Shengshan Hu, Minghui Li +4
\textit{Federated learning} (FL) is a nascent distributed learning paradigm to train a shared global model without violating users' privacy. FL has been shown to be vulnerable to v…
Why Does Little Robustness Help? A Further Step Towards Understanding Adversarial Transferability
Yechao Zhang, Shengshan Hu, Leo Yu Zhang +5
Adversarial examples (AEs) for DNNs have been shown to be transferable: AEs that successfully fool white-box surrogate models can also deceive other black-box models with different…
Detecting Backdoors During the Inference Stage Based on Corruption Robustness Consistency
Xiaogeng Liu, Minghui Li, Haoyu Wang +5
Deep neural networks are proven to be vulnerable to backdoor attacks. Detecting the trigger samples during the inference stage, i.e., the test-time trigger sample detection, can pr…
Shielding Federated Learning: Mitigating Byzantine Attacks with Less Constraints
Minghui Li, Wei Wan, Jianrong Lu +5
Federated learning is a newly emerging distributed learning framework that facilitates the collaborative training of a shared global model among distributed participants with their…
Protecting Facial Privacy: Generating Adversarial Identity Masks via Style-robust Makeup Transfer
Shengshan Hu, Xiaogeng Liu, Yechao Zhang +4
While deep face recognition (FR) systems have shown amazing performance in identification and verification, they also arouse privacy concerns for their excessive surveillance on us…
Optimizing Privacy-Preserving Outsourced Convolutional Neural Network Predictions
Minghui Li, Sherman S. M. Chow, Shengshan Hu +3
Convolutional neural network is a machine-learning model widely applied in various prediction tasks, such as computer vision and medical image analysis. Their great predictive powe…
Augmenting Encrypted Search: A Decentralized Service Realization with Enforced Execution
Shengshan Hu, Chengjun Cai, Qian Wang +4
Searchable symmetric encryption (SSE) allows the data owner to outsource an encrypted database to a remote server in a private manner while maintaining the ability for selectively…