Abstract

Recent research has revealed that the output of Deep Neural Networks (DNN) can be easily altered by adding relatively small perturbations to the input vector. In this paper, we analyze an attack in an extremely limited scenario where only one pixel can be modified. For that we propose a novel method for generating one-pixel adversarial perturbations based on differential evolution (DE). It requires less adversarial information (a black-box attack) and can fool more types of networks due to the inherent features of DE. The results show that 67.97% of the natural images in Kaggle CIFAR-10 test dataset and 16.04% of the ImageNet (ILSVRC 2012) test images can be perturbed to at least one target class by modifying just one pixel with 74.03% and 22.91% confidence on average. We also show the same vulnerability on the original CIFAR-10 dataset. Thus, the proposed attack explores a different take on adversarial machine learning in an extreme limited scenario, showing that current DNNs are also vulnerable to such low dimension attacks. Besides, we also illustrate an important application of DE (or broadly speaking, evolutionary computation) in the domain of adversarial machine learning: creating tools that can effectively generate low-cost adversarial attacks against neural networks for evaluating robustness.

Affiliated Institutions

Related Publications

Publication Info

Year
2019
Type
article
Volume
23
Issue
5
Pages
828-841
Citations
1578
Access
Closed

Social Impact

Social media, news, blog, policy document mentions

Citation Metrics

1578
OpenAlex
122
Influential
1560
CrossRef

Cite This

Jiawei Su, Danilo Vasconcellos Vargas, Kouichi Sakurai et al. (2019). One Pixel Attack for Fooling Deep Neural Networks. IEEE Transactions on Evolutionary Computation , 23 (5) , 828-841. https://doi.org/10.1109/tevc.2019.2890858

Identifiers

DOI
10.1109/tevc.2019.2890858
arXiv
1710.08864

Data Quality

Data completeness: 84%