https://github.com/qdata/deepcloak
ICLR16: DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples
https://github.com/qdata/deepcloak
adversarial-samples mask residual-networks robustness
Last synced: about 1 year ago
JSON representation
ICLR16: DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples
- Host: GitHub
- URL: https://github.com/qdata/deepcloak
- Owner: QData
- License: apache-2.0
- Created: 2017-05-22T14:13:51.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2019-08-28T16:29:08.000Z (almost 7 years ago)
- Last Synced: 2025-04-06T17:05:57.561Z (about 1 year ago)
- Topics: adversarial-samples, mask, residual-networks, robustness
- Language: Lua
- Homepage: https://arxiv.org/abs/1702.06763
- Size: 10.7 KB
- Stars: 6
- Watchers: 4
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples
## Environment: Torch7 + CUDNN
## Reference
at Workshop of ICLR16:
### Title: DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples
### [URL: https://arxiv.org/abs/1702.06763](https://arxiv.org/abs/1702.06763)
```
@article{GaoWQ17,
author = {Ji Gao and
Beilun Wang and
Yanjun Qi},
title = {DeepCloak: Masking {DNN} Models for robustness against adversarial
samples},
journal = {CoRR},
volume = {abs/1702.06763},
year = {2017},
url = {http://arxiv.org/abs/1702.06763},
archivePrefix = {arXiv},
eprint = {1702.06763},
biburl = {https://dblp.org/rec/bib/journals/corr/GaoWQ17},
}
```
## Example:
th removenode.lua -dataset resources/cifar10.t7 -model resources/model_res-164.t7 -layernum 8
## Usage:
th removenode.lua -model MODELADD -dataset DATASETADD -layernum LAYERNUM -std STD [-power POWER] [-gpu GPUNUM]
* [MODELADD]: address of the model file \n
* [LAYERNUM]: number of the layer where the mask will be inserted after it
* [POWER]: attack strength, epsilon in Fast Gradient Sign Method, default 10
* [GPUNUM]: number of GPU selected
* [DATASETADD]: address of the dataset file
* [STD]: the standard deviation of the dataset used in the preprocessing, required in the Adversarial Sample Generation
## Dataset and models: Orginially from https://github.com/szagoruyko/wide-residual-networks
* Download them from http://www.cs.virginia.edu/~jg6yd/resources/
* Cifar-10: Whitened data of CIFAR-10, std = 17.067, which is selected as the default dataset
* model_vgg_orig.t7: A pretrained model of VGG-16
* model_res-164.t7: A pretrained model of the 152 layer residual network
* Wide.t7: A pretrained model of wide residual network from https://github.com/szagoruyko/wide-residual-networks