Exploring the Space of Adversarial Images
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Renaming mnist module (conflict with torch mnist)
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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



GFortran with BLAS


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:

cd lbfgsb
make lib

This library is as fast the Torch7 Optim's LBFGS (wihout bound constraints).


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:

cd mnist
th download.lua


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):

cd overfeat
sh install.sh

Adversarial images

Now you can create adversarial images using:

th adversarial.lua -i image.png


-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.