pytorch-cw2

Introduction

This is a rich-documented PyTorch implementation of Carlini-Wanger's L2 attack. The main reason to develop this respository is to make it easier to do research using the attach technique. Another implementation in PyTorch is rwightman/pytorch-nips2017-attack-example. However, the author failed to reproduce the result presented in the original paper (by Aug 2, 2018 at least).

cw.py has been tested under python 2.7.12 and torch-0.3.1.

References:

Usage of this library module

First of all, make sure the import runutils statement in cw.py (line 19) is a valid import statement in your development environment.

In the following code sample, we assume that net is a pretrained network, such that outputs = net(torch.autograd.Variable(inputs)) returns a torch.autograd.Variable of dimension (batch_size, num_classes) if inputs is of dimension (batch_size, num_channels, height, width). Assume also that when doing normalization to the inputs, the normalization transformation is presented something like:

normalization = torchvision.transforms.Normalize(mean, std)

where mean and std are both 3-tuples of floats.

One more thing to notice is that when producing adversarial examples from inputs, cw.py prints debugging information. To suppress such behavior, use

sed -i '/FIXME$/d' cw.py

to delete all printing statements.

Code example

To make the following code snippet executable, these variables need to be assigned:

dataloader: the dataloader (of type torch.utils.data.DataLoader)
mean: the mean used in inputs normalization
std: the standard deviation used in inputs normalization

import torch
import cw

inputs_box = (min((0 - m) / s for m, s in zip(mean, std)),
              max((1 - m) / s for m, s in zip(mean, std)))
# an untargeted adversary
adversary = cw.L2Adversary(targeted=False,
                           confidence=0.0,
                           search_steps=10,
                           box=inputs_box,
                           optimizer_lr=5e-4)

inputs, targets = next(iter(dataloader))
adversarial_examples = adversary(net, inputs, targets, to_numpy=False)
assert isinstance(adversarial_examples, torch.FloatTensor)
assert adversarial_examples.size() == inputs.size()

# a targeted adversary
adversary = cw.L2Adversary(targeted=True,
                           confidence=0.0,
                           search_steps=10,
                           box=inputs_box,
                           optimizer_lr=5e-4)

inputs, _ = next(iter(dataloader))
# a batch of any attack targets
attack_targets = torch.ones(inputs.size(0)) * 3
adversarial_examples = adversary(net, inputs, attack_targets, to_numpy=False)
assert isinstance(adversarial_examples, torch.FloatTensor)
assert adversarial_examples.size() == inputs.size()

What's to_numpy parameter? In the above examples, if it were True, adversarial_examples would be of type numpy.ndarray. This behavior might be desirable if one would like to store the adversarial examples in compressed npz format using numpy.

kkew3/pytorch-cw2

pytorch-cw2

Introduction

References:

Usage of this library module

Code example