Low-Frequency Black-Box Backdoor Attack via Evolutionary Algorithm

no code implementations • 23 Feb 2024 • Yanqi Qiao, Dazhuang Liu, Rui Wang, Kaitai Liang

Extensive experiments on real-world datasets verify the effectiveness and robustness of LFBA against image processing operations and the state-of-the-art backdoor defenses, as well as its inherent stealthiness in both spatial and frequency space, making it resilient against frequency inspection.

Backdoor Attack

FTA: Stealthy and Adaptive Backdoor Attack with Flexible Triggers on Federated Learning

no code implementations • 31 Aug 2023 • Yanqi Qiao, Dazhuang Liu, Congwen Chen, Rui Wang, Kaitai Liang

In this work, we propose a new stealthy and robust backdoor attack with flexible triggers against FL defenses.

Backdoor Attack Federated Learning

MULTI-FLGANs: Multi-Distributed Adversarial Networks for Non-IID distribution

no code implementations • 24 Jun 2022 • Akash Amalan, Rui Wang, Yanqi Qiao, Emmanouil Panaousis, Kaitai Liang

Federated learning is an emerging concept in the domain of distributed machine learning.

Federated Learning

FLVoogd: Robust And Privacy Preserving Federated Learning

no code implementations • 24 Jun 2022 • Yuhang Tian, Rui Wang, Yanqi Qiao, Emmanouil Panaousis, Kaitai Liang

In this work, we propose FLVoogd, an updated federated learning method in which servers and clients collaboratively eliminate Byzantine attacks while preserving privacy.

Federated Learning Image Classification +1