7 citations · 14 across the 6 of their papers we have counts for
6 papers
A Certified Radius-Guided Attack Framework to Image Segmentation Models
Wenjie Qu, Youqi Li, Binghui Wang
Image segmentation is an important problem in many safety-critical applications. Recent studies show that modern image segmentation models are vulnerable to adversarial perturbatio…
REaaS: Enabling Adversarially Robust Downstream Classifiers via Robust Encoder as a Service
Wenjie Qu, Jinyuan Jia, Neil Zhenqiang Gong
Encoder as a service is an emerging cloud service. Specifically, a service provider first pre-trains an encoder (i.e., a general-purpose feature extractor) via either supervised le…
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…
MultiGuard: Provably Robust Multi-label Classification against Adversarial Examples
Jinyuan Jia, Wenjie Qu, Neil Zhenqiang Gong
Multi-label classification, which predicts a set of labels for an input, has many applications. However, multiple recent studies showed that multi-label classification is vulnerabl…
jTrans: Jump-Aware Transformer for Binary Code Similarity
Hao Wang, Wenjie Qu, Gilad Katz +5
Binary code similarity detection (BCSD) has important applications in various fields such as vulnerability detection, software component analysis, and reverse engineering. Recent s…
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…