Journal Article10.1109/TEVC.2019.2890858
One Pixel Attack for Fooling Deep Neural Networks
TL;DR: This paper proposes a novel method for generating one-pixel adversarial perturbations based on differential evolution (DE), which requires less adversarial information (a black-box attack) and can fool more types of networks due to the inherent features of DE.
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Abstract: Recent research has revealed that the output of deep neural networks (DNNs) 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.
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Citations
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A Black-box Attack on Neural Networks Based on Swarm Evolutionary Algorithm
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Multi-Model Selective Backdoor Attack with Different Trigger Positions
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Evolutionary Algorithms for Convolutional Neural Network Visualisation
Nicolas Bernard,Franck Leprévost +1 more
- 26 Sep 2018
TL;DR: Deep Learning is based on deep neural networks trained over huge sets of examples, which enabled computers to compete with—or even outperform—humans at many tasks, from playing Go to driving vehicules.
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A Thorough Comparison Study on Adversarial Attacks and Defenses for Common Thorax Disease Classification in Chest X-rays.
TL;DR: It is found that the attack and defense methods for common thorax disease classification in chest X-rays have poor performance with excessive iterations and large perturbations, so a new defense method is proposed that is robust to different degrees of perturbation.
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