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# Copyright (c) 2019-present, Francesco Croce
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import time
import torch
from autoattack.fab_projections import projection_linf, projection_l2,\
projection_l1
DEFAULT_EPS_DICT_BY_NORM = {'Linf': .3, 'L2': 1., 'L1': 5.0}
class FABAttack():
"""
Fast Adaptive Boundary Attack (Linf, L2, L1)
https://arxiv.org/abs/1907.02044
:param norm: Lp-norm to minimize ('Linf', 'L2', 'L1' supported)
:param n_restarts: number of random restarts
:param n_iter: number of iterations
:param eps: epsilon for the random restarts
:param alpha_max: alpha_max
:param eta: overshooting
:param beta: backward step
"""
def __init__(
self,
norm='Linf',
n_restarts=1,
n_iter=100,
eps=None,
alpha_max=0.1,
eta=1.05,
beta=0.9,
loss_fn=None,
verbose=False,
seed=0,
targeted=False,
device=None,
n_target_classes=9):
""" FAB-attack implementation in pytorch """
self.norm = norm
self.n_restarts = n_restarts
self.n_iter = n_iter
self.eps = eps if eps is not None else DEFAULT_EPS_DICT_BY_NORM[norm]
self.alpha_max = alpha_max
self.eta = eta
self.beta = beta
self.targeted = targeted
self.verbose = verbose
self.seed = seed
self.target_class = None
self.device = device
self.n_target_classes = n_target_classes
def check_shape(self, x):
return x if len(x.shape) > 0 else x.unsqueeze(0)
def _predict_fn(self, x):
raise NotImplementedError("Virtual function.")
def _get_predicted_label(self, x):
raise NotImplementedError("Virtual function.")
def get_diff_logits_grads_batch(self, imgs, la):
raise NotImplementedError("Virtual function.")
def get_diff_logits_grads_batch_targeted(self, imgs, la, la_target):
raise NotImplementedError("Virtual function.")
def attack_single_run(self, x, y=None, use_rand_start=False, is_targeted=False):
"""
:param x: clean images
:param y: clean labels, if None we use the predicted labels
:param is_targeted True if we ise targeted version. Targeted class is assigned by `self.target_class`
"""
if self.device is None:
self.device = x.device
self.orig_dim = list(x.shape[1:])
self.ndims = len(self.orig_dim)
x = x.detach().clone().float().to(self.device)
#assert next(self.predict.parameters()).device == x.device
y_pred = self._get_predicted_label(x)
if y is None:
y = y_pred.detach().clone().long().to(self.device)
else:
y = y.detach().clone().long().to(self.device)
pred = y_pred == y
corr_classified = pred.float().sum()
if self.verbose:
print('Clean accuracy: {:.2%}'.format(pred.float().mean()))
if pred.sum() == 0:
return x
pred = self.check_shape(pred.nonzero().squeeze())
if is_targeted:
output = self._predict_fn(x)
la_target = output.sort(dim=-1)[1][:, -self.target_class]
la_target2 = la_target[pred].detach().clone()
startt = time.time()
# runs the attack only on correctly classified points
im2 = x[pred].detach().clone()
la2 = y[pred].detach().clone()
if len(im2.shape) == self.ndims:
im2 = im2.unsqueeze(0)
bs = im2.shape[0]
u1 = torch.arange(bs)
adv = im2.clone()
adv_c = x.clone()
res2 = 1e10 * torch.ones([bs]).to(self.device)
x1 = im2.clone()
x0 = im2.clone().reshape([bs, -1])
if use_rand_start:
if self.norm == 'Linf':
t = 2 * torch.rand(x1.shape).to(self.device) - 1
x1 = im2 + (torch.min(res2,
self.eps * torch.ones(res2.shape)
.to(self.device)
).reshape([-1, *[1]*self.ndims])
) * t / (t.reshape([t.shape[0], -1]).abs()
.max(dim=1, keepdim=True)[0]
.reshape([-1, *[1]*self.ndims])) * .5
elif self.norm == 'L2':
t = torch.randn(x1.shape).to(self.device)
x1 = im2 + (torch.min(res2,
self.eps * torch.ones(res2.shape)
.to(self.device)
).reshape([-1, *[1]*self.ndims])
) * t / ((t ** 2)
.view(t.shape[0], -1)
.sum(dim=-1)
.sqrt()
.view(t.shape[0], *[1]*self.ndims)) * .5
elif self.norm == 'L1':
t = torch.randn(x1.shape).to(self.device)
x1 = im2 + (torch.min(res2,
self.eps * torch.ones(res2.shape)
.to(self.device)
).reshape([-1, *[1]*self.ndims])
) * t / (t.abs().view(t.shape[0], -1)
.sum(dim=-1)
.view(t.shape[0], *[1]*self.ndims)) / 2
x1 = x1.clamp(0.0, 1.0)
counter_iter = 0
while counter_iter < self.n_iter:
with torch.no_grad():
if is_targeted:
df, dg = self.get_diff_logits_grads_batch_targeted(x1, la2, la_target2)
else:
df, dg = self.get_diff_logits_grads_batch(x1, la2)
if self.norm == 'Linf':
dist1 = df.abs() / (1e-12 +
dg.abs()
.reshape(dg.shape[0], dg.shape[1], -1)
.sum(dim=-1))
elif self.norm == 'L2':
dist1 = df.abs() / (1e-12 + (dg ** 2)
.reshape(dg.shape[0], dg.shape[1], -1)
.sum(dim=-1).sqrt())
elif self.norm == 'L1':
dist1 = df.abs() / (1e-12 + dg.abs().reshape(
[df.shape[0], df.shape[1], -1]).max(dim=2)[0])
else:
raise ValueError('norm not supported')
ind = dist1.min(dim=1)[1]
dg2 = dg[u1, ind]
b = (- df[u1, ind] + (dg2 * x1).reshape(x1.shape[0], -1)
.sum(dim=-1))
w = dg2.reshape([bs, -1])
if self.norm == 'Linf':
d3 = projection_linf(
torch.cat((x1.reshape([bs, -1]), x0), 0),
torch.cat((w, w), 0),
torch.cat((b, b), 0))
elif self.norm == 'L2':
d3 = projection_l2(
torch.cat((x1.reshape([bs, -1]), x0), 0),
torch.cat((w, w), 0),
torch.cat((b, b), 0))
elif self.norm == 'L1':
d3 = projection_l1(
torch.cat((x1.reshape([bs, -1]), x0), 0),
torch.cat((w, w), 0),
torch.cat((b, b), 0))
d1 = torch.reshape(d3[:bs], x1.shape)
d2 = torch.reshape(d3[-bs:], x1.shape)
if self.norm == 'Linf':
a0 = d3.abs().max(dim=1, keepdim=True)[0]\
.view(-1, *[1]*self.ndims)
elif self.norm == 'L2':
a0 = (d3 ** 2).sum(dim=1, keepdim=True).sqrt()\
.view(-1, *[1]*self.ndims)
elif self.norm == 'L1':
a0 = d3.abs().sum(dim=1, keepdim=True)\
.view(-1, *[1]*self.ndims)
a0 = torch.max(a0, 1e-8 * torch.ones(
a0.shape).to(self.device))
a1 = a0[:bs]
a2 = a0[-bs:]
alpha = torch.min(torch.max(a1 / (a1 + a2),
torch.zeros(a1.shape)
.to(self.device)),
self.alpha_max * torch.ones(a1.shape)
.to(self.device))
x1 = ((x1 + self.eta * d1) * (1 - alpha) +
(im2 + d2 * self.eta) * alpha).clamp(0.0, 1.0)
is_adv = self._get_predicted_label(x1) != la2
if is_adv.sum() > 0:
ind_adv = is_adv.nonzero().squeeze()
ind_adv = self.check_shape(ind_adv)
if self.norm == 'Linf':
t = (x1[ind_adv] - im2[ind_adv]).reshape(
[ind_adv.shape[0], -1]).abs().max(dim=1)[0]
elif self.norm == 'L2':
t = ((x1[ind_adv] - im2[ind_adv]) ** 2)\
.reshape(ind_adv.shape[0], -1).sum(dim=-1).sqrt()
elif self.norm == 'L1':
t = (x1[ind_adv] - im2[ind_adv])\
.abs().reshape(ind_adv.shape[0], -1).sum(dim=-1)
adv[ind_adv] = x1[ind_adv] * (t < res2[ind_adv]).\
float().reshape([-1, *[1]*self.ndims]) + adv[ind_adv]\
* (t >= res2[ind_adv]).float().reshape(
[-1, *[1]*self.ndims])
res2[ind_adv] = t * (t < res2[ind_adv]).float()\
+ res2[ind_adv] * (t >= res2[ind_adv]).float()
x1[ind_adv] = im2[ind_adv] + (
x1[ind_adv] - im2[ind_adv]) * self.beta
counter_iter += 1
ind_succ = res2 < 1e10
if self.verbose:
print('success rate: {:.0f}/{:.0f}'
.format(ind_succ.float().sum(), corr_classified) +
' (on correctly classified points) in {:.1f} s'
.format(time.time() - startt))
ind_succ = self.check_shape(ind_succ.nonzero().squeeze())
adv_c[pred[ind_succ]] = adv[ind_succ].clone()
return adv_c
def perturb(self, x, y):
if self.device is None:
self.device = x.device
adv = x.clone()
with torch.no_grad():
acc = self._predict_fn(x).max(1)[1] == y
startt = time.time()
torch.random.manual_seed(self.seed)
torch.cuda.random.manual_seed(self.seed)
if not self.targeted:
for counter in range(self.n_restarts):
ind_to_fool = acc.nonzero().squeeze()
if len(ind_to_fool.shape) == 0: ind_to_fool = ind_to_fool.unsqueeze(0)
if ind_to_fool.numel() != 0:
x_to_fool, y_to_fool = x[ind_to_fool].clone(), y[ind_to_fool].clone()
adv_curr = self.attack_single_run(x_to_fool, y_to_fool, use_rand_start=(counter > 0), is_targeted=False)
acc_curr = self._predict_fn(adv_curr).max(1)[1] == y_to_fool
if self.norm == 'Linf':
res = (x_to_fool - adv_curr).abs().reshape(x_to_fool.shape[0], -1).max(1)[0]
elif self.norm == 'L2':
res = ((x_to_fool - adv_curr) ** 2).reshape(x_to_fool.shape[0], -1).sum(dim=-1).sqrt()
elif self.norm == 'L1':
res = (x_to_fool - adv_curr).abs().reshape(x_to_fool.shape[0], -1).sum(-1)
acc_curr = torch.max(acc_curr, res > self.eps)
ind_curr = (acc_curr == 0).nonzero().squeeze()
acc[ind_to_fool[ind_curr]] = 0
adv[ind_to_fool[ind_curr]] = adv_curr[ind_curr].clone()
if self.verbose:
print('restart {} - robust accuracy: {:.2%} at eps = {:.5f} - cum. time: {:.1f} s'.format(
counter, acc.float().mean(), self.eps, time.time() - startt))
else:
for target_class in range(2, self.n_target_classes + 2):
self.target_class = target_class
for counter in range(self.n_restarts):
ind_to_fool = acc.nonzero().squeeze()
if len(ind_to_fool.shape) == 0: ind_to_fool = ind_to_fool.unsqueeze(0)
if ind_to_fool.numel() != 0:
x_to_fool, y_to_fool = x[ind_to_fool].clone(), y[ind_to_fool].clone()
adv_curr = self.attack_single_run(x_to_fool, y_to_fool, use_rand_start=(counter > 0), is_targeted=True)
acc_curr = self._predict_fn(adv_curr).max(1)[1] == y_to_fool
if self.norm == 'Linf':
res = (x_to_fool - adv_curr).abs().reshape(x_to_fool.shape[0], -1).max(1)[0]
elif self.norm == 'L2':
res = ((x_to_fool - adv_curr) ** 2).reshape(x_to_fool.shape[0], -1).sum(dim=-1).sqrt()
elif self.norm == 'L1':
res = (x_to_fool - adv_curr).abs().reshape(x_to_fool.shape[0], -1).sum(-1)
acc_curr = torch.max(acc_curr, res > self.eps)
ind_curr = (acc_curr == 0).nonzero().squeeze()
acc[ind_to_fool[ind_curr]] = 0
adv[ind_to_fool[ind_curr]] = adv_curr[ind_curr].clone()
if self.verbose:
print('restart {} - target_class {} - robust accuracy: {:.2%} at eps = {:.5f} - cum. time: {:.1f} s'.format(
counter, self.target_class, acc.float().mean(), self.eps, time.time() - startt))
return adv
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