RESEARCH

THEORY

First-order Adversarial Vulnerability of Neural Networks and Input Dimension

June 9, 2019

Abstract

Over the past few years, neural networks were proven vulnerable to adversarial images: targeted but imperceptible image perturbations lead to drastically different predictions. We show that adversarial vulnerability increases with the gradients of the training objective when viewed as a function of the inputs. Surprisingly, vulnerability does not depend on network topology: for many standard network architectures, we prove that at initialization, the l1-norm of these gradients grows as the square root of the input dimension, leaving the networks increasingly vulnerable with growing image size. We empirically show that this dimension dependence persists after either usual or robust training, but gets attenuated with higher regularization.

Download the Paper

Related Publications

June 02, 2019

Simple Attention-Based Representation Learning for Ranking Short Social Media Posts | Facebook AI Research

This paper explores the problem of ranking short social media posts with respect to user queries using neural networks. Instead of starting with a complex architecture, we proceed from the bottom up and examine the effectiveness of a simple,…

Peng Shi, Jinfeng Rao, Jimmy Lin

June 02, 2019

June 09, 2019

THEORY

First-order Adversarial Vulnerability of Neural Networks and Input Dimension | Facebook AI Research

Over the past few years, neural networks were proven vulnerable to adversarial images: targeted but imperceptible image perturbations lead to drastically different predictions. We show that adversarial vulnerability increases with the gradients…

Carl-Johann Simon-Gabriel, Yann Ollivier, Bernhard Scholkopf, Leon Bottou, David Lopez-Paz

June 09, 2019

May 31, 2019

INTEGRITY

Abusive Language Detection with Graph Convolutional Networks | Facebook AI Research

Abuse on the Internet represents a significant societal problem of our time. Previous research on automated abusive language detection in Twitter has shown that community-based profiling of users is a promising technique for this task. However,…

Pushkar Mishra, Marco Del Tredici, Helen Yannakoudakis, Ekaterina Shutova

May 31, 2019

June 01, 2019

Probabilistic Planning with Reduced Models | Facebook AI Research

Reduced models are simplified versions of a given domain, designed to accelerate the planning process. Interest in reduced models has grown since the surprising success of determinization in the first international probabilistic planning…

Luis Pineda, Shlomo Zilberstein

June 01, 2019

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.