A
Ananthram Swami
Researcher at United States Army Research Laboratory
Publications - 488
Citations - 30179
Ananthram Swami is an academic researcher from United States Army Research Laboratory. The author has contributed to research in topics: Wireless network & Communication channel. The author has an hindex of 59, co-authored 468 publications receiving 24564 citations. Previous affiliations of Ananthram Swami include University of Southern California & University of Minnesota.
Papers
More filters
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
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%.
Proceedings ArticleDOI
metapath2vec: Scalable Representation Learning for Heterogeneous Networks
TL;DR: Two scalable representation learning models, namely metapath2vec and metapATH2vec++, are developed that are able to not only outperform state-of-the-art embedding models in various heterogeneous network mining tasks, but also discern the structural and semantic correlations between diverse network objects.
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.