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https://github.com/tabacof/adversarial
Exploring the Space of Adversarial Images
https://github.com/tabacof/adversarial
Last synced: 12 days ago
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Exploring the Space of Adversarial Images
- Host: GitHub
- URL: https://github.com/tabacof/adversarial
- Owner: tabacof
- License: gpl-3.0
- Created: 2015-01-15T00:30:52.000Z (almost 10 years ago)
- Default Branch: master
- Last Pushed: 2016-08-02T20:46:06.000Z (over 8 years ago)
- Last Synced: 2024-07-01T22:15:25.966Z (4 months ago)
- Language: FORTRAN
- Homepage:
- Size: 4.01 MB
- Stars: 69
- Watchers: 10
- Forks: 19
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Exploring the Space of Adversarial Images
> Tabacof, Pedro and Valle, Eduardo. Exploring the Space of Adversarial Images. arXiv preprint arXiv:1510.05328, 2015.
Please cite us if you use this code. [ArXiv link](http://arxiv.org/abs/1510.05328)
## Requirements
[Torch7](https://github.com/torch/torch7)
GFortran with BLAS
## L-BFGS-B
The adversarial image optimization problem requires the box-constraints so that the distortions won't make the image go outside the pixel space (RGB = [0, 255]).
For this we use the Fortran library L-BFGS-B written by Nocedal, the author of the algorithm. To compile the library do the following:
```bash
cd lbfgsb
make lib
```
This library is as fast the Torch7 Optim's LBFGS (wihout bound constraints).
## MNIST
For MNIST, the code will train the classifier from scratch. A logistic regression should achieve about 7.5% error, and a standard convolutional network 1%. You need to download the dataset:
```bash
cd mnist
th download.lua
```
## ImageNet
For ImageNet you can use the pre-trained OverFeat network, which is a deep convolutional neural network that won the localization ILSVRC competition in 2013.
First you must download the weights of the network (thanks to Jonghoon Jin):
```bash
cd overfeat
sh install.sh
```
## Adversarial images
Now you can create adversarial images using:
```bash
th adversarial.lua -i image.png
```
Options:
```
-i: image file
-cuda: use GPU support (must have CUDA installed on your computer - test this with require 'cutorch')
-gpu: GPU device number
-ub: unbounded optimization (allow the distortion to go outside the pixel space)
-mc: probe the space around the adversarial image using white noise (default is Gaussian)
-hist: use nonparametric noise instead of Gaussian ("histogram")
-orig: probe the space around the original image instead
-numbermc: number of probes
-mnist: use MNIST instead of ImageNet dataset
-conv: use convolutional network with MNIST (instead of logistic regression)
-itorch: iTorch plotting
-seed: random seed
```
The resulting images and the distortions will be created on the same folder of the image.