8 citations · 12 across the 4 of their papers we have counts for
4 papers
Nearest is Not Dearest: Towards Practical Defense against Quantization-conditioned Backdoor Attacks
Boheng Li, Yishuo Cai, Haowei Li +3
Model quantization is widely used to compress and accelerate deep neural networks. However, recent studies have revealed the feasibility of weaponizing model quantization via impla…
FDINet: Protecting against DNN Model Extraction via Feature Distortion Index
Hongwei Yao, Zheng Li, Haiqin Weng +3
Machine Learning as a Service (MLaaS) platforms have gained popularity due to their accessibility, cost-efficiency, scalability, and rapid development capabilities. However, recent…
Noise Doesn't Lie: Towards Universal Detection of Deep Inpainting
Ang Li, Qiuhong Ke, Xingjun Ma +4
Deep image inpainting aims to restore damaged or missing regions in an image with realistic contents. While having a wide range of applications such as object removal and image rec…
Practical Privacy Attacks on Vertical Federated Learning
Haiqin Weng, Juntao Zhang, Xingjun Ma +4
Federated learning (FL) is a privacy-preserving learning paradigm that allows multiple parities to jointly train a powerful machine learning model without sharing their private dat…