paper-conference

VOID: Backdoor Injection through Knowledge Vacuity in Federated Unlearning
Federated unlearning (FU) enables federated systems to remove designated data from a trained global model, but its security risks …
Wenwei Zhao, Yuxuan Xie, Haiyun Liu, Jie Xu, Zhuo Lu
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VOID: Backdoor Injection through Knowledge Vacuity in Federated Unlearning
ScrambleMark: Zero-Query Watermark-Agnostic Black-Box Evasion of Modern Speech Watermarking
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Binhao Ma, Weikang Ding, Hanqing Guo, Wenwei Zhao, Xiaowen Li, Leah Ding, Zhuo Lu, Rui Duan
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Adapter-Based Parameter-Efficient Adversarial Training for Enhanced Clean Accuracy
Adversarial training has emerged as a leading defense against adversarial attacks in deep learning, but suffers from a fundamental …
Xiaowen Li, Wenwei Zhao, Yao liu, Zhuo Lu
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Adapter-Based Parameter-Efficient Adversarial Training for Enhanced Clean Accuracy
Consistency-Preserving Logit Shaping for Robust Model Stealing Defense with Applications in Wireless Spectrum Security
Model stealing (model extraction) threatens ML-as-a-service APIs by enabling adversaries to reconstruct proprietary models from queried …
Xiaowen Li, Wenwei Zhao, Yao liu, Zhuo Lu
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Malicious Forgetting: Backdoor Injection in Active Federated Unlearning and Countermeasure Design
Federated learning (FL) enables collaborative model training without sharing raw data, but also raises increasing demands for the right …
Wenwei Zhao, Yuanzhe Peng, Xiaowen Li, Jie Xu, Yao Liu, Zhuo Lu
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Malicious Forgetting: Backdoor Injection in Active Federated Unlearning and Countermeasure Design
Adversarial Update-Based Federated Unlearning for Poisoned Model Recovery
Federated learning (FL) is vulnerable to poisoning attacks, where malicious clients upload manipulated updates to degrade the …
Wenwei Zhao, Xiaowen Li, Yao Liu, Zhuo Lu
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Adversarial Update-Based Federated Unlearning for Poisoned Model Recovery
Equilibrium-Driven Vertical Federated Learning with Selective Privacy Protection
Vertical Federated Learning (VFL) enables multiple clients with feature-partitioned data to collaboratively train models while …
Yuanzhe Peng, Wenwei Zhao, Zhuo Lu, Jie Xu
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Equilibrium-Driven Vertical Federated Learning with Selective Privacy Protection
Detecting Adversarial Spectrum Attacks via Distance to Decision Boundary Statistics
In this paper, we propose an efficient framework for detecting adversarial spectrum attacks. Our design leverages the concept of the …
Wenwei Zhao, Xiaowen Li, Shangqing Zhao, Jie Xu, Yao liu, Zhuo Lu
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Detecting Adversarial Spectrum Attacks via Distance to Decision Boundary Statistics