Quanquan Gu | Security Research Group

Merlin, Morgan, and the Importance of Thresholds and Priors

2 October 2020 privacy, privacy-preserving machine learning, Bargav Jayaraman, Lingxiao Wang, Katherine Knipmeyer, Quanquan Gu

Post by Katherine Knipmeyer Machine learning poses a substantial risk that adversaries will be able to discover information that the model does not intend to reveal. One set of methods by which consumers can learn this sensitive information, known broadly as membership inference attacks, predicts whether or not a query record belongs to the training set. A basic membership inference attack involves an attacker with a given record and black-box access to a model who tries to determine whether said record was a member of the model’s training set.

Intrinsic Robustness using Conditional GANs

14 August 2020 adversarial machine learning, AISTATS, Xiao Zhang, Jinghui Chen, Quanquan Gu

The video of Xiao’s presentation for AISTATS 2020 is now available: Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models Starting with Gilmer et al. (2018), several works have demonstrated the inevitability of adversarial examples based on different assumptions about the underlying input probability space. It remains unclear, however, whether these results apply to natural image distributions. In this work, we assume the underlying data distribution is captured by some conditional generative model, and prove intrinsic robustness bounds for a general class of classifiers, which solves an open problem in Fawzi et al.