most citedAttention Hijacking in Trojan Transformers

8 citations · 20 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG20233 cited

Attention-Enhancing Backdoor Attacks Against BERT-based Models

Weimin Lyu, Songzhu Zheng, Lu Pang +2

Recent studies have revealed that \textit{Backdoor Attacks} can threaten the safety of natural language processing (NLP) models. Investigating the strategies of backdoor attacks wi…

cs.CR20236 cited

Client-side Gradient Inversion Against Federated Learning from Poisoning

Jiaheng Wei, Yanjun Zhang, Leo Yu Zhang +5

Federated Learning (FL) enables distributed participants (e.g., mobile devices) to train a global model without sharing data directly to a central server. Recent studies have revea…

cs.CR2023

model-based script synthesis for fuzzing

Zian Liu, Chao Chen, Muhammad Ejaz Ahmed +2

Kernel fuzzing is important for finding critical kernel vulnerabilities. Close-source (e.g., Windows) operating system kernel fuzzing is even more challenging due to the lack of so…

eess.IV20233 cited

Learning to Segment from Noisy Annotations: A Spatial Correction Approach

Jiachen Yao, Yikai Zhang, Songzhu Zheng +3

Noisy labels can significantly affect the performance of deep neural networks (DNNs). In medical image segmentation tasks, annotations are error-prone due to the high demand in ann…

cs.LG20228 cited

Attention Hijacking in Trojan Transformers

Weimin Lyu, Songzhu Zheng, Tengfei Ma +2

Trojan attacks pose a severe threat to AI systems. Recent works on Transformer models received explosive popularity and the self-attentions are now indisputable. This raises a cent…