← Search

Virat Shejwalkar

2 accepted papers

2023

The Perils of Learning From Unlabeled Data: Backdoor Attacks on Semi-supervised Learning

ICCV 2023poster

Semi-supervised learning (SSL) is gaining popularity as it reduces cost of machine learning (ML) by training high performance models using unlabeled data. In this paper, we reveal that the key feature of SSL, i.e., learning from (non-inspected) unlabeled data, exposes SSL to strong poisoning attacks…

Cited by 14PDFcodeScholar
2021

Membership Privacy for Machine Learning Models Through Knowledge Transfer

AAAI 2021technical

Large capacity machine learning (ML) models are prone to membership inference attacks (MIAs), which aim to infer whether the target sample is a member of the target model's training dataset. The serious privacy concerns due to the membership inference have motivated multiple defenses against MIAs, e…