31 citations · 142 across the 21 of their papers we have counts for
6 papers · 1 filter
TrojFair: Trojan Fairness Attacks
Mengxin Zheng, Jiaqi Xue, Yi Sheng +3
Deep learning models have been incorporated into high-stakes sectors, including healthcare diagnosis, loan approvals, and candidate recruitment, among others. Consequently, any bia…
TrojFSP: Trojan Insertion in Few-shot Prompt Tuning
Mengxin Zheng, Jiaqi Xue, Xun Chen +3
Prompt tuning is one of the most effective solutions to adapting a fixed pre-trained language model (PLM) for various downstream tasks, especially with only a few input samples. Ho…
Audit and Improve Robustness of Private Neural Networks on Encrypted Data
Jiaqi Xue, Lei Xu, Lin Chen +3
Performing neural network inference on encrypted data without decryption is one popular method to enable privacy-preserving neural networks (PNet) as a service. Compared with regul…
TrojViT: Trojan Insertion in Vision Transformers
Mengxin Zheng, Qian Lou, Lei Jiang
Vision Transformers (ViTs) have demonstrated the state-of-the-art performance in various vision-related tasks. The success of ViTs motivates adversaries to perform backdoor attacks…
Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data
Qian Lou, Bo Feng, Geoffrey C. Fox +1
Big data is one of the cornerstones to enabling and training deep neural networks (DNNs). Because of the lack of expertise, to gain benefits from their data, average users have to…
AutoQ: Automated Kernel-Wise Neural Network Quantization
Qian Lou, Feng Guo, Lantao Liu +2
Network quantization is one of the most hardware friendly techniques to enable the deployment of convolutional neural networks (CNNs) on low-power mobile devices. Recent network qu…