This repository contains samples for generating adversarial examples in torch, using autograd (https://github.com/twitter/torch-autograd) for Automatic Differentiation.

###Run sample (Train/Test NN)###

• th sample_name.lua -runOnCuda
• th sample_name.lua -runOnCuda -printAdversarial
• qlua sample_name.lua -runOnCuda -printAdversarial -showImages
• -printAdversarial option takes more time (in order to compute the network outputs for the adversarial examples)

##Dataset## A dataset object has 3 DatasetSplits

• {trainset, testset, validset}
• number of classes (for classification tasks)

##DatasetSplits##

• mean, stdv, classLabels for the dataset

##Model## A model contains the net architecture, the criterion and the feval function (forward and backward steps). It also contains code for generating adversarial examples.

• net
• criterion
• feval(), forward(), backward()
• adversarialSamples()

###ModelResnetAdversarial###

• trained with adversarial cost
• evaluated on its own adversarial examples (w.r.t. its own adversarial cost)

##Trainer## A trainer contains details about:

• number of iteration, number of epochs, mini-batch size
• parameters update algorithm (function and parameters: ex.sgd with learningRate 0.1)
• printing out metrics about the training (accuracy, accuracy on adversarial examples)
• plotting adversarial images

###Help###

• th sample_name.lua -h

##Adversarial examples for ResNet20 on CIFAR10 General setup:

• simple training (not fine tuned)
• CIFAR10 raw data (preprocess: normalization only)
• grad_cost_x = dcost(x)/dx
• adversarial_x(x) = x + grad_cost_x/norm(grad_cost_x)

1. Training with normal cost: ClassNLLCriterion(x)
2. Training with adversarial cost: (ClassNLLCriterion(x) + ClassNLLCriterion(adversarial_x))/2

Metric \ Cost type	Normal Cost	Adversarial Cost
Accuracy	73%	68%
Accuracy on adversarial examples	29%	57%
% Adversarial samples with changed label	53%	34%
Visually
Visually
Visually