50 citations · 185 across the 23 of their papers we have counts for
6 papers · 1 filter
Causality & Control Flow
Robert Künnemann, Deepak Garg, Michael Backes
Causality has been the issue of philosophic debate since Hippocrates. It is used in formal verification and testing, e.g., to explain counterexamples or construct fault trees. Rece…
Adversarial Vulnerability Bounds for Gaussian Process Classification
Michael Thomas Smith, Kathrin Grosse, Michael Backes +1
Machine learning (ML) classification is increasingly used in safety-critical systems. Protecting ML classifiers from adversarial examples is crucial. We propose that the main threa…
Proconda -- Protected Control Data
Marie-Therese Walter, David Pfaff, Stefan Nürnberger +1
Memory corruption vulnerabilities often enable attackers to take control of a target system by overwriting control-flow relevant data (such as return addresses and function pointer…
MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples
Jinyuan Jia, Ahmed Salem, Michael Backes +2
In a membership inference attack, an attacker aims to infer whether a data sample is in a target classifier's training dataset or not. Specifically, given a black-box access to the…
Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning
Ahmed Salem, Apratim Bhattacharya, Michael Backes +2
Machine learning (ML) has progressed rapidly during the past decade and the major factor that drives such development is the unprecedented large-scale data. As data generation is a…
On the security relevance of weights in deep learning
Kathrin Grosse, Thomas A. Trost, Marius Mosbach +2
Recently, a weight-based attack on stochastic gradient descent inducing overfitting has been proposed. We show that the threat is broader: A task-independent permutation on the ini…