Federated learning (FL) enables collaborative model training without sharing raw data, but its robustness is vulnerable to Byzantine clients, especially under non-identically distributed (non-IID) data. In heterogeneous FL, benign client updates may become multimodal and statistically diverse, making it difficult for m...
Shu-Juan Tian, Shu-Huan Xiang, Gang Liu et al.· IEEE Transactions on Neural...· 0 citations
ODPure is proposed, a novel input-stage black-box defense for object detection, which is based on input purification that ensures stable perception flows and provides robust defense against diverse backdoor attacks and trigger types while preserving baseline accuracy.
Li Zeng, Ming-Cheng Duan, Long-Fei Fan et al.· 0 citations
Vision-Language Models (VLMs) are known to be vulnerable to adversarial attacks, where subtle perturbations to images or texts induce erroneous outputs. However, most text-based attacks are adapted from language-model-centric methods, in which the visual input is fixed during optimization, resulting in adversarial prom...
Li Zeng, Ze-Yu Ye, Meng Xie et al.· arXiv.org· 0 citations
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