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Formalizing and Estimating Distribution Inference Risks
Anshuman Suri, David Evans
Distribution inference, sometimes called property inference, infers statistical properties about a training set from access to a model trained on that data. Distribution inference…
Understanding Intrinsic Robustness Using Label Uncertainty
Xiao Zhang, David Evans
A fundamental question in adversarial machine learning is whether a robust classifier exists for a given task. A line of research has made some progress towards this goal by studyi…
Formalizing Distribution Inference Risks
Anshuman Suri, David Evans
Property inference attacks reveal statistical properties about a training set but are difficult to distinguish from the primary purposes of statistical machine learning, which is t…
Stealthy Backdoors as Compression Artifacts
Yulong Tian, Fnu Suya, Fengyuan Xu +1
In a backdoor attack on a machine learning model, an adversary produces a model that performs well on normal inputs but outputs targeted misclassifications on inputs containing a s…
Improved Estimation of Concentration Under -Norm Distance Metrics Using Half Spaces
Jack Prescott, Xiao Zhang, David Evans
Concentration of measure has been argued to be the fundamental cause of adversarial vulnerability. Mahloujifar et al. presented an empirical way to measure the concentration of a d…