50 citations · 157 across the 32 of their papers we have counts for
15 papers · 1 filter
ECLIPSE: Expunging Clean-label Indiscriminate Poisons via Sparse Diffusion Purification
Xianlong Wang, Shengshan Hu, Yechao Zhang +5
Clean-label indiscriminate poisoning attacks add invisible perturbations to correctly labeled training images, thus dramatically reducing the generalization capability of the victi…
Large Language Model Watermark Stealing With Mixed Integer Programming
Zhaoxi Zhang, Xiaomei Zhang, Yanjun Zhang +5
The Large Language Model (LLM) watermark is a newly emerging technique that shows promise in addressing concerns surrounding LLM copyright, monitoring AI-generated text, and preven…
DarkFed: A Data-Free Backdoor Attack in Federated Learning
Minghui Li, Wei Wan, Yuxuan Ning +4
Federated learning (FL) has been demonstrated to be susceptible to backdoor attacks. However, existing academic studies on FL backdoor attacks rely on a high proportion of real cli…
Towards Model Extraction Attacks in GAN-Based Image Translation via Domain Shift Mitigation
Di Mi, Yanjun Zhang, Leo Yu Zhang +4
Model extraction attacks (MEAs) enable an attacker to replicate the functionality of a victim deep neural network (DNN) model by only querying its API service remotely, posing a se…
Robust Backdoor Detection for Deep Learning via Topological Evolution Dynamics
Xiaoxing Mo, Yechao Zhang, Leo Yu Zhang +5
A backdoor attack in deep learning inserts a hidden backdoor in the model to trigger malicious behavior upon specific input patterns. Existing detection approaches assume a metric…
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…
Masked Language Model Based Textual Adversarial Example Detection
Xiaomei Zhang, Zhaoxi Zhang, Qi Zhong +4
Adversarial attacks are a serious threat to the reliable deployment of machine learning models in safety-critical applications. They can misguide current models to predict incorrec…
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…
Evaluating Membership Inference Through Adversarial Robustness
Zhaoxi Zhang, Leo Yu Zhang, Xufei Zheng +2
The usage of deep learning is being escalated in many applications. Due to its outstanding performance, it is being used in a variety of security and privacy-sensitive areas in add…
Towards Privacy-Preserving Neural Architecture Search
Fuyi Wang, Leo Yu Zhang, Lei Pan +2
Machine learning promotes the continuous development of signal processing in various fields, including network traffic monitoring, EEG classification, face identification, and many…
Shielding Federated Learning: Robust Aggregation with Adaptive Client Selection
Wei Wan, Shengshan Hu, Jianrong Lu +3
Federated learning (FL) enables multiple clients to collaboratively train an accurate global model while protecting clients' data privacy. However, FL is susceptible to Byzantine a…
Challenges and Approaches for Mitigating Byzantine Attacks in Federated Learning
Junyu Shi, Wei Wan, Shengshan Hu +2
Recently emerged federated learning (FL) is an attractive distributed learning framework in which numerous wireless end-user devices can train a global model with the data remained…
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…
Shielding Collaborative Learning: Mitigating Poisoning Attacks through Client-Side Detection
Lingchen Zhao, Shengshan Hu, Qian Wang +4
Collaborative learning allows multiple clients to train a joint model without sharing their data with each other. Each client performs training locally and then submits the model u…
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…