Simple Black-Box Adversarial Perturbations for Deep Networks

TL;DR

This paper demonstrates that deep neural networks are vulnerable to simple black-box adversarial attacks that require minimal knowledge and perturb only a few pixels, highlighting a significant security concern.

Contribution

The authors introduce simple, effective black-box attack methods that craft adversarial examples by perturbing a small number of pixels without internal network knowledge.

Findings

Elementary attacks can successfully fool deep networks

Perturbing a few pixels causes misclassification

Attacks are effective even with minimal information

Abstract

Deep neural networks are powerful and popular learning models that achieve state-of-the-art pattern recognition performance on many computer vision, speech, and language processing tasks. However, these networks have also been shown susceptible to carefully crafted adversarial perturbations which force misclassification of the inputs. Adversarial examples enable adversaries to subvert the expected system behavior leading to undesired consequences and could pose a security risk when these systems are deployed in the real world. In this work, we focus on deep convolutional neural networks and demonstrate that adversaries can easily craft adversarial examples even without any internal knowledge of the target network. Our attacks treat the network as an oracle (black-box) and only assume that the output of the network can be observed on the probed inputs. Our first attack is based on a simple idea of adding perturbation to a randomly selected single pixel or a small set of them. We then improve the effectiveness of this attack by carefully constructing a small set of pixels to perturb by using the idea of greedy local-search. Our proposed attacks also naturally extend to a stronger notion of misclassification. Our extensive experimental results illustrate that even these elementary attacks can reveal a deep neural network's vulnerabilities. The simplicity and effectiveness of our proposed schemes mean that they could serve as a litmus test for designing robust networks.