2.3k citations · 6.5k across the 41 of their papers we have counts for
10 papers · 1 filter
Towards the Science of Security and Privacy in Machine Learning
Nicolas Papernot, Patrick McDaniel, Arunesh Sinha +1
Advances in machine learning (ML) in recent years have enabled a dizzying array of applications such as data analytics, autonomous systems, and security diagnostics. ML is now perv…
Patient-Driven Privacy Control through Generalized Distillation
Z. Berkay Celik, David Lopez-Paz, Patrick McDaniel
The introduction of data analytics into medicine has changed the nature of patient treatment. In this, patients are asked to disclose personal information such as genetic markers,…
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
Nicolas Papernot, Fartash Faghri, Nicholas Carlini +23
CleverHans is a software library that provides standardized reference implementations of adversarial example construction techniques and adversarial training. The library may be us…
On the Effectiveness of Defensive Distillation
Nicolas Papernot, Patrick McDaniel
We report experimental results indicating that defensive distillation successfully mitigates adversarial samples crafted using the fast gradient sign method, in addition to those c…
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…