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README.md

Fast Feature Fool

Code for the paper Fast Feature Fool: A data independent approach to universal adversarial perturbations

Konda Reddy Mopuri, Utsav Garg, R. Venkatesh Babu

This repository can be used to generate universal adversarial perturbations for 5 popular CNNs(VGG-F, CaffeNet, VGG-16, VGG-19, GoogLeNet) without using any data from the target distrubution.

sample perturbations

Usage

  • To generate a new perturbation give the following command
python train.py --network vggf
  • To evaluate the classification and fooling performace of the generated perturbation
python evaluate.py --network vggf --adv_im perturbations/perturbation_vggf_mean.npy --img_list <path to ilsvrc val list> --gt_labels <path to validation labels list>
  • The network argument can take the following five arguments vggf, caffenet, vgg16, vgg19 and googlenet

Precomputed perturbations

The perturbations has precomputed perturbations for five cnns,

Named perturbation_<cnn name>_mean.npy, use mean of activations as the loss function, these were used to report results in the paper.

Network Top-1 Accuracy Fooling Rate
VGG-F 53.43% 81.59%
CaffeNet 56.02% 80.92%
VGG-16 65.77% 47.10%
VGG-19 66.14% 43.62%
GoogLeNet 67.92% 56.44%

Reference

@inproceedings{mopuri-bmvc-2017,
  title={Fast Feature Fool: A data independent approach to universal adversarial perturbations},
  author={Mopuri, Konda Reddy and Garg, Utsav and Babu, R Venkatesh},
 booktitle = {Proceedings of the British Machine Vision Conference ({BMVC})},
 year = {2017}
}

Contact Utsav Garg if you have questions.

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Data independent universal adversarial perturbations

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