3 citations · 4 across the 3 of their papers we have counts for
4 papers
Pre-trained Encoders in Self-Supervised Learning Improve Secure and Privacy-preserving Supervised Learning
Hongbin Liu, Wenjie Qu, Jinyuan Jia +1
Classifiers in supervised learning have various security and privacy issues, e.g., 1) data poisoning attacks, backdoor attacks, and adversarial examples on the security side as wel…
EncoderMI: Membership Inference against Pre-trained Encoders in Contrastive Learning
Hongbin Liu, Jinyuan Jia, Wenjie Qu +1
Given a set of unlabeled images or (image, text) pairs, contrastive learning aims to pre-train an image encoder that can be used as a feature extractor for many downstream tasks. I…
PointGuard: Provably Robust 3D Point Cloud Classification
Hongbin Liu, Jinyuan Jia, Neil Zhenqiang Gong
3D point cloud classification has many safety-critical applications such as autonomous driving and robotic grasping. However, several studies showed that it is vulnerable to advers…
On the Intrinsic Differential Privacy of Bagging
Hongbin Liu, Jinyuan Jia, Neil Zhenqiang Gong
Differentially private machine learning trains models while protecting privacy of the sensitive training data. The key to obtain differentially private models is to introduce noise…