S
Somesh Jha
Researcher at University of Wisconsin-Madison
Publications - 363
Citations - 36185
Somesh Jha is an academic researcher from University of Wisconsin-Madison. The author has contributed to research in topics: Computer science & Model checking. The author has an hindex of 76, co-authored 328 publications receiving 29859 citations. Previous affiliations of Somesh Jha include University of Wisconsin–Milwaukee & University of Stuttgart.
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Proceedings ArticleDOI
The Limitations of Deep Learning in Adversarial Settings
Nicolas Papernot,Patrick McDaniel,Somesh Jha,Matt Fredrikson,Z. Berkay Celik,Ananthram Swami +5 more
TL;DR: This work formalizes the space of adversaries against deep neural networks (DNNs) and introduces a novel class of algorithms to craft adversarial samples based on a precise understanding of the mapping between inputs and outputs of DNNs.
Proceedings ArticleDOI
Practical Black-Box Attacks against Machine Learning
TL;DR: This work introduces the first practical demonstration of an attacker controlling a remotely hosted DNN with no such knowledge, and finds that this black-box attack strategy is capable of evading defense strategies previously found to make adversarial example crafting harder.
Proceedings ArticleDOI
Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures
TL;DR: A new class of model inversion attack is developed that exploits confidence values revealed along with predictions and is able to estimate whether a respondent in a lifestyle survey admitted to cheating on their significant other and recover recognizable images of people's faces given only their name.
Proceedings ArticleDOI
Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks
TL;DR: In this article, the authors introduce a defensive mechanism called defensive distillation to reduce the effectiveness of adversarial samples on DNNs, which increases the average minimum number of features that need to be modified to create adversarial examples by about 800%.
Posted Content
The Limitations of Deep Learning in Adversarial Settings
Nicolas Papernot,Patrick McDaniel,Somesh Jha,Matt Fredrikson,Z. Berkay Celik,Ananthram Swami +5 more
TL;DR: In this paper, the authors formalize the space of adversaries against deep neural networks and introduce a novel class of algorithms to craft adversarial samples based on a precise understanding of the mapping between inputs and outputs of DNNs.