2.3k citations · 8.3k across the 36 of their papers we have counts for
5 papers · 1 filter
Data-Free Model Extraction
Jean-Baptiste Truong, Pratyush Maini, Robert J. Walls +1
Current model extraction attacks assume that the adversary has access to a surrogate dataset with characteristics similar to the proprietary data used to train the victim model. Th…
Adversarial Examples in Constrained Domains
Ryan Sheatsley, Nicolas Papernot, Michael Weisman +2
Machine learning algorithms have been shown to be vulnerable to adversarial manipulation through systematic modification of inputs (e.g., adversarial examples) in domains such as i…
Tempered Sigmoid Activations for Deep Learning with Differential Privacy
Nicolas Papernot, Abhradeep Thakurta, Shuang Song +2
Because learning sometimes involves sensitive data, machine learning algorithms have been extended to offer privacy for training data. In practice, this has been mostly an aftertho…
Label-Only Membership Inference Attacks
Christopher A. Choquette-Choo, Florian Tramer, Nicholas Carlini +1
Membership inference attacks are one of the simplest forms of privacy leakage for machine learning models: given a data point and model, determine whether the point was used to tra…
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations
Florian Tramèr, Jens Behrmann, Nicholas Carlini +2
Adversarial examples are malicious inputs crafted to induce misclassification. Commonly studied sensitivity-based adversarial examples introduce semantically-small changes to an in…