579 citations · 1.2k across the 13 of their papers we have counts for
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Adversarial Perturbations Against Deep Neural Networks for Malware Classification
Kathrin Grosse, Nicolas Papernot, Praveen Manoharan +2
Deep neural networks, like many other machine learning models, have recently been shown to lack robustness against adversarially crafted inputs. These inputs are derived from regul…
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow
Many machine learning models are vulnerable to adversarial examples: inputs that are specially crafted to cause a machine learning model to produce an incorrect output. Adversarial…
Crafting Adversarial Input Sequences for Recurrent Neural Networks
Nicolas Papernot, Patrick McDaniel, Ananthram Swami +1
Machine learning models are frequently used to solve complex security problems, as well as to make decisions in sensitive situations like guiding autonomous vehicles or predicting…