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.
- 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
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% |
@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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