37 citations · 82 across the 5 of their papers we have counts for
9 papers
Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture
Xinyu Tang, Saeed Mahloujifar, Liwei Song +4
Membership inference attacks are a key measure to evaluate privacy leakage in machine learning (ML) models. These attacks aim to distinguish training members from non-members by ex…
A Critical Evaluation of Open-World Machine Learning
Liwei Song, Vikash Sehwag, Arjun Nitin Bhagoji +1
Open-world machine learning (ML) combines closed-world models trained on in-distribution data with out-of-distribution (OOD) detectors, which aim to detect and reject OOD inputs. P…
Universal Adversarial Attacks with Natural Triggers for Text Classification
Liwei Song, Xinwei Yu, Hsuan-Tung Peng +1
Recent work has demonstrated the vulnerability of modern text classifiers to universal adversarial attacks, which are input-agnostic sequences of words added to text processed by c…
Systematic Evaluation of Privacy Risks of Machine Learning Models
Liwei Song, Prateek Mittal
Machine learning models are prone to memorizing sensitive data, making them vulnerable to membership inference attacks in which an adversary aims to guess if an input sample was us…
Towards Probabilistic Verification of Machine Unlearning
David Marco Sommer, Liwei Song, Sameer Wagh +1
The right to be forgotten, also known as the right to erasure, is the right of individuals to have their data erased from an entity storing it. The status of this long held notion…
Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples
Vikash Sehwag, Arjun Nitin Bhagoji, Liwei Song +4
A large body of recent work has investigated the phenomenon of evasion attacks using adversarial examples for deep learning systems, where the addition of norm-bounded perturbation…