6 papers · 1 filter
AMRM-Pure: Semantic-Preserving Adversarial Purification
Zhihao Dou, Zhiqiang Gao, Dongfei Cui +6
Adversarial purification is a defense technique that employs generative models to remove adversarial perturbations. Current methods often rely on powerful generators, typically dif…
ReShift: Aha-Moment-Driven Reasoning-Level Backdoor Attacks on Vision-Language Models
Zhihao Dou, Qinjian Zhao, Zhiqiang Gao +1
Vision--Language Models (VLMs) are increasingly deployed in safety-critical applications, yet remain vulnerable to backdoor attacks. Existing methods primarily manipulate final out…
ProtoGuard-SL: Prototype Consistency Based Backdoor Defense for Vertical Split Learning
Yuhan Shui, Ruobin Jin, Zhihao Dou +1
Vertical split learning (SL) enables collaborative model training across parties holding complementary features without sharing raw data, but recent work has shown that it is highl…
SecureSplit: Mitigating Backdoor Attacks in Split Learning
Zhihao Dou, Dongfei Cui, Weida Wang +7
Split Learning (SL) offers a framework for collaborative model training that respects data privacy by allowing participants to share the same dataset while maintaining distinct fea…
Toward Malicious Clients Detection in Federated Learning
Zhihao Dou, Jiaqi Wang, Wei Sun +2
Federated learning (FL) enables multiple clients to collaboratively train a global machine learning model without sharing their raw data. However, the decentralized nature of FL in…
Adversarial Attacks to Multi-Modal Models
Zhihao Dou, Xin Hu, Haibo Yang +2
Multi-modal models have gained significant attention due to their powerful capabilities. These models effectively align embeddings across diverse data modalities, showcasing superi…