Poster

Recovering the input of neural networks via single shot side-channel attacks

Conference paper (2019)

Authors

Lejla Batina Radboud Universiteit Nijmegen
Shivam Bhasin Nanyang Technological University
Dirmanto Jap Nanyang Technological University
More Info
expand_more

Published Date

2019

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

The interplay between machine learning and security is becoming more prominent. New applications using machine learning also bring new security risks. Here, we show it is possible to reverse-engineer the inputs to a neural network with only a single-shot side-channel measurement assuming the attacker knows the neural network architecture being used.

Files

P2657_batina.pdf
(pdf | 5.18 Mb)
Unknown license

Download not available