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attacks.py
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attacks.py
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"""Generates adversarial example for Caffe networks."""
import numpy as np
import caffe
__author__ = 'Anurag Arnab'
__copyright__ = 'Copyright (c) 2018, Anurag Arnab'
__credits__ = ['Anurag Arnab', 'Ondrej Miksik', 'Philip Torr']
__email__ = '[email protected]'
__license__ = 'MIT'
def fgsm(net, x, eps):
r"""Caffe implementation of the Fast Gradient Sign Method.
This attack was proposed in
net: The Caffe network. Must have its weights initialised already
Makes the following assumptions
- force_backward is set to "true" so that gradients are computed
- Has two inputs: "data" and "label"
- Has two outputs: "output" and "loss"
x: The input data. We will find an adversarial example using this.
- Assume that x.shape = net.blobs['data'].shape
eps: l_{\infty} norm of the perturbation that will be generated
Returns the adversarial example, as well as just the pertubation
(adversarial example - original input)
"""
shape_label = net.blobs['label'].data.shape
dummy_label = np.zeros(shape_label)
net.blobs['data'].data[0,:,:,:] = np.squeeze(x)
net.blobs['label'].data[...] = dummy_label
net.forward()
net_prediction = net.blobs['output'].data[0].argmax(axis=0).astype(np.uint32)
net.blobs['label'].data[...] = net_prediction
data_diff = net.backward(diffs=['data'])
grad_data = data_diff['data']
signed_grad = np.sign(grad_data) * eps
adversarial_x = x + signed_grad
return adversarial_x, signed_grad
def IterativeFGSM(net, x, eps, num_iters=-1, alpha=1, do_stop_max_pert=False):
r"""Iterative FGSM.
net: The caffe net. See the docstring for "fgsm" for the assumptions
x: The input image
eps: l_{\infty} norm of the perturbation
num_iters: The number of iterations to run for. If it is negative, the formula
used from Kurakin et al. Adversarial Machine Learning at Scale ICLR 2016 is used
do_stop_max_pert: If this is true, the optimisation runs until either the max-norm
constraint is reached, or num_iters is reached.
"""
clip_min = x - eps
clip_max = x + eps
if num_iters <= 0:
num_iters = np.min([eps + 4, 1.25*eps]) # Used in Kurakin et al. ICLR 2016
num_iters = int(np.max([np.ceil(num_iters), 1]))
adversarial_x = x
shape_label = net.blobs['label'].data.shape
dummy_label = np.zeros(shape_label)
net.blobs['label'].data[...] = dummy_label
for i in range(num_iters):
net.blobs['data'].data[0,:,:,:] = np.squeeze(adversarial_x)
net.forward()
net_prediction = net.blobs['output'].data[0].argmax(axis=0).astype(np.uint32)
if i == 0:
net.blobs['label'].data[...] = net_prediction
data_diff = net.backward(diffs=['data'])
grad_data = data_diff['data']
signed_grad = np.sign(grad_data) * alpha
adversarial_x = np.clip(adversarial_x + signed_grad, clip_min, clip_max)
adv_perturbation = adversarial_x - x
if do_stop_max_pert:
max_pert = np.max(np.abs(adv_perturbation))
if max_pert >= eps: # Due to floating point inaccuracies, need >= instead of just ==
print "Stopping after {} iterations: Max norm reached".format(i+1)
break
return adversarial_x, adv_perturbation
def IterativeFGSMLeastLikely(net, x, eps, num_iters=-1, alpha=1, do_stop_max_pert=False):
r"""Iterative FGSM Least Likely.
This attack was proposed in Kurakin et al. Adversarial Machine Learning at Scale. ICLR 2016.
net: The caffe net. See the docstring for "fgsm" for the assumptions
x: The input image
eps: l_{\infty} norm of the perturbation
num_iters: The number of iterations to run for. If it is negative, the formula
used from Kurakin et al. is used.
do_stop_max_pert: If this is true, the optimisation runs until either the max-norm
constraint is reached, or num_iters is reached.
"""
clip_min = x - eps
clip_max = x + eps
if num_iters <= 0:
num_iters = np.min([eps + 4, 1.25*eps]) # Used in Kurakin et al. ICLR 2016
num_iters = int(np.max([np.ceil(num_iters), 1]))
adversarial_x = x
shape_label = net.blobs['label'].data.shape
dummy_label = np.zeros(shape_label)
for i in range(num_iters):
net.blobs['data'].data[0,:,:,:] = np.squeeze(adversarial_x)
net.blobs['label'].data[...] = dummy_label
net.forward()
net_predictions = np.argsort(-net.blobs['output'].data[0], axis=0)
target_idx = net_predictions.shape[0] - 1
target = net_predictions[target_idx]
target = np.squeeze(target)
net.blobs['label'].data[...] = target
grads = net.backward(diffs=['data'])
grad_data = grads['data']
signed_grad = np.sign(grad_data) * alpha
adversarial_x = np.clip(adversarial_x - signed_grad, clip_min, clip_max)
adv_perturbation = adversarial_x - x
if do_stop_max_pert:
max_pert = np.max(np.abs(adv_perturbation))
if max_pert >= eps: # Due to floating point inaccuracies, need >= instead of just ==
print "Stopping after {} iterations: Max norm reached".format(i+1)
break
return adversarial_x, adv_perturbation
def fgsm_targetted(net, x, eps, target_idx):
r"""Targetted FGSM attack.
net: The caffe net. See the docstring for "fgsm" for the assumptions
x: The input image
eps: l_{\infty} norm of the perturbation
target_idx: The class that the adversarial attack is targetted for,
Note, that this is not the class id, but rather the relative ranking (0 indexed.
In other words, target_idx=1 means that the target will be the class
that was predicted with the second highest confidence.
"""
shape_label = net.blobs['label'].data.shape
dummy_label = np.zeros(shape_label)
net.blobs['data'].data[0,:,:,:] = np.squeeze(x)
net.blobs['label'].data[...] = dummy_label
net.forward()
net_predictions = np.argsort(-net.blobs['output'].data[0], axis=0)
if (target_idx < 0 or target_idx > net_predictions.shape[0]):
raise ValueError("Target idx should be an integer in the range [0,num_classes-1]")
target = net_predictions[target_idx]
target = np.squeeze(target)
net.blobs['label'].data[...] = target
grads = net.backward(diffs=['data'])
grad_data = grads['data']
signed_grad = np.sign(grad_data) * eps
adversarial_x = x - signed_grad
return adversarial_x, -signed_grad