Towards Deep Learning Models Resistant to Adversarial Attacks

06/19/2017
by   Aleksander Madry, et al.
0

Recent work has demonstrated that neural networks are vulnerable to adversarial examples, i.e., inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network. In fact, some of the latest findings suggest that the existence of adversarial attacks may be an inherent weakness of deep learning models. To address this problem, we study the adversarial robustness of neural networks through the lens of robust optimization. This approach provides us with a broad and unifying view on much of the prior work on this topic. Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal. In particular, they specify a concrete security guarantee that would protect against any adversary. These methods let us train networks with significantly improved resistance to a wide range of adversarial attacks. They also suggest the notion of security against a first-order adversary as a natural and broad security guarantee. We believe that robustness against such well-defined classes of adversaries is an important stepping stone towards fully resistant deep learning models.

READ FULL TEXT

Authors

page 1

page 2

page 3

page 4

09/12/2019

Feedback Learning for Improving the Robustness of Neural Networks

Recent research studies revealed that neural networks are vulnerable to ...
06/18/2020

The Dilemma Between Dimensionality Reduction and Adversarial Robustness

Recent work has shown the tremendous vulnerability to adversarial sample...
11/18/2019

Hacking Neural Networks: A Short Introduction

A large chunk of research on the security issues of neural networks is f...
07/01/2020

Robust Learning against Logical Adversaries

Test-time adversarial attacks have posed serious challenges to the robus...
06/04/2020

Characterizing the Weight Space for Different Learning Models

Deep Learning has become one of the primary research areas in developing...
11/02/2020

Frequency-based Automated Modulation Classification in the Presence of Adversaries

Automatic modulation classification (AMC) aims to improve the efficiency...
05/28/2021

Visualizing Representations of Adversarially Perturbed Inputs

It has been shown that deep learning models are vulnerable to adversaria...

Code Repositories

cifar10_challenge

A challenge to explore adversarial robustness of neural networks on CIFAR10.


view repo

mnist_challenge

A challenge to explore adversarial robustness of neural networks on MNIST.


view repo
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.