34 citations · 59 across the 7 of their papers we have counts for
12 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…
FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information
Xiaoyu Cao, Jinyuan Jia, Zaixi Zhang +1
Federated learning is vulnerable to poisoning attacks in which malicious clients poison the global model via sending malicious model updates to the server. Existing defenses focus…
FLCert: Provably Secure Federated Learning against Poisoning Attacks
Xiaoyu Cao, Zaixi Zhang, Jinyuan Jia +1
Due to its distributed nature, federated learning is vulnerable to poisoning attacks, in which malicious clients poison the training process via manipulating their local training d…
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…
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…
BadEncoder: Backdoor Attacks to Pre-trained Encoders in Self-Supervised Learning
Jinyuan Jia, Yupei Liu, Neil Zhenqiang Gong
Self-supervised learning in computer vision aims to pre-train an image encoder using a large amount of unlabeled images or (image, text) pairs. The pre-trained image encoder can th…