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# Copyright (c) 2020-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 torch
import time
import math
import torch.nn.functional as F
from autoattack.autopgd_base import L1_projection
class SquareAttack():
"""
Square Attack
https://arxiv.org/abs/1912.00049
:param predict: forward pass function
:param norm: Lp-norm of the attack ('Linf', 'L2' supported)
:param n_restarts: number of random restarts
:param n_queries: max number of queries (each restart)
:param eps: bound on the norm of perturbations
:param seed: random seed for the starting point
:param p_init: parameter to control size of squares
:param loss: loss function optimized ('margin', 'ce' supported)
:param resc_schedule adapt schedule of p to n_queries
"""
def __init__(
self,
predict,
norm='Linf',
n_queries=5000,
eps=None,
p_init=.8,
n_restarts=1,
seed=0,
verbose=False,
targeted=False,
loss='margin',
resc_schedule=True,
device=None):
"""
Square Attack implementation in PyTorch
"""
self.predict = predict
self.norm = norm
self.n_queries = n_queries
self.eps = eps
self.p_init = p_init
self.n_restarts = n_restarts
self.seed = seed
self.verbose = verbose
self.targeted = targeted
self.loss = loss
self.rescale_schedule = resc_schedule
self.device = device
self.return_all = False
def margin_and_loss(self, x, y):
"""
:param y: correct labels if untargeted else target labels
"""
logits = self.predict(x)
xent = F.cross_entropy(logits, y, reduction='none')
u = torch.arange(x.shape[0])
y_corr = logits[u, y].clone()
logits[u, y] = -float('inf')
y_others = logits.max(dim=-1)[0]
if not self.targeted:
if self.loss == 'ce':
return y_corr - y_others, -1. * xent
elif self.loss == 'margin':
return y_corr - y_others, y_corr - y_others
else:
return y_others - y_corr, xent
def init_hyperparam(self, x):
assert self.norm in ['Linf', 'L2', 'L1']
assert not self.eps is None
assert self.loss in ['ce', 'margin']
if self.device is None:
self.device = x.device
self.orig_dim = list(x.shape[1:])
self.ndims = len(self.orig_dim)
if self.seed is None:
self.seed = time.time()
def random_target_classes(self, y_pred, n_classes):
y = torch.zeros_like(y_pred)
for counter in range(y_pred.shape[0]):
l = list(range(n_classes))
l.remove(y_pred[counter])
t = self.random_int(0, len(l))
y[counter] = l[t]
return y.long().to(self.device)
def check_shape(self, x):
return x if len(x.shape) == (self.ndims + 1) else x.unsqueeze(0)
def random_choice(self, shape):
t = 2 * torch.rand(shape).to(self.device) - 1
return torch.sign(t)
def random_int(self, low=0, high=1, shape=[1]):
t = low + (high - low) * torch.rand(shape).to(self.device)
return t.long()
def normalize(self, x):
if self.norm == 'Linf':
t = x.abs().view(x.shape[0], -1).max(1)[0]
return x / (t.view(-1, *([1] * self.ndims)) + 1e-12)
elif self.norm == 'L2':
t = (x ** 2).view(x.shape[0], -1).sum(-1).sqrt()
return x / (t.view(-1, *([1] * self.ndims)) + 1e-12)
elif self.norm == 'L1':
t = x.abs().view(x.shape[0], -1).sum(dim=-1)
return x / (t.view(-1, *([1] * self.ndims)) + 1e-12)
def lp_norm(self, x):
if self.norm == 'L2':
t = (x ** 2).view(x.shape[0], -1).sum(-1).sqrt()
return t.view(-1, *([1] * self.ndims))
elif self.norm == 'L1':
t = x.abs().view(x.shape[0], -1).sum(dim=-1)
return t.view(-1, *([1] * self.ndims))
def eta_rectangles(self, x, y):
delta = torch.zeros([x, y]).to(self.device)
x_c, y_c = x // 2 + 1, y // 2 + 1
counter2 = [x_c - 1, y_c - 1]
if self.norm == 'L2':
for counter in range(0, max(x_c, y_c)):
delta[max(counter2[0], 0):min(counter2[0] + (2*counter + 1), x),
max(0, counter2[1]):min(counter2[1] + (2*counter + 1), y)
] += 1.0/(torch.Tensor([counter + 1]).view(1, 1).to(
self.device) ** 2)
counter2[0] -= 1
counter2[1] -= 1
delta /= (delta ** 2).sum(dim=(0, 1), keepdim=True).sqrt()
elif self.norm == 'L1':
for counter in range(0, max(x_c, y_c)):
delta[max(counter2[0], 0):min(counter2[0] + (2*counter + 1), x),
max(0, counter2[1]):min(counter2[1] + (2*counter + 1), y)
] += 1.0/(torch.Tensor([counter + 1]).view(1, 1).to(
self.device) ** 4)
counter2[0] -= 1
counter2[1] -= 1
delta /= delta.abs().sum(dim=(), keepdim=True)
return delta
def eta(self, s):
if self.norm == 'L2':
delta = torch.zeros([s, s]).to(self.device)
delta[:s // 2] = self.eta_rectangles(s // 2, s)
delta[s // 2:] = -1. * self.eta_rectangles(s - s // 2, s)
delta /= (delta ** 2).sum(dim=(0, 1), keepdim=True).sqrt()
elif self.norm == 'L1':
delta = torch.zeros([s, s]).to(self.device)
delta[:s // 2] = self.eta_rectangles(s // 2, s)
delta[s // 2:] = -1. * self.eta_rectangles(s - s // 2, s)
#delta = self.eta_rectangles(s, s)
delta /= delta.abs().sum(dim=(), keepdim=True)
#delta *= (torch.rand([1]) - .5).sign().to(self.device)
if torch.rand([1]) > 0.5:
delta = delta.permute([1, 0])
return delta
def p_selection(self, it):
""" schedule to decrease the parameter p """
if self.rescale_schedule:
it = int(it / self.n_queries * 10000)
if 10 < it <= 50:
p = self.p_init / 2
elif 50 < it <= 200:
p = self.p_init / 4
elif 200 < it <= 500:
p = self.p_init / 8
elif 500 < it <= 1000:
p = self.p_init / 16
elif 1000 < it <= 2000:
p = self.p_init / 32
elif 2000 < it <= 4000:
p = self.p_init / 64
elif 4000 < it <= 6000:
p = self.p_init / 128
elif 6000 < it <= 8000:
p = self.p_init / 256
elif 8000 < it:
p = self.p_init / 512
else:
p = self.p_init
return p
def attack_single_run(self, x, y):
with torch.no_grad():
adv = x.clone()
c, h, w = x.shape[1:]
n_features = c * h * w
n_ex_total = x.shape[0]
if self.verbose and h != w:
print('square attack may not work properly for non-square image.')
print('for details please refer to https://github.com/fra31/auto-attack/issues/95')
if self.norm == 'Linf':
x_best = torch.clamp(x + self.eps * self.random_choice(
[x.shape[0], c, 1, w]), 0., 1.)
margin_min, loss_min = self.margin_and_loss(x_best, y)
n_queries = torch.ones(x.shape[0]).to(self.device)
s_init = int(math.sqrt(self.p_init * n_features / c))
if (margin_min < 0.0).all():
return n_queries, x_best
for i_iter in range(self.n_queries):
idx_to_fool = (margin_min > 0.0).nonzero().squeeze()
x_curr = self.check_shape(x[idx_to_fool])
x_best_curr = self.check_shape(x_best[idx_to_fool])
y_curr = y[idx_to_fool]
if len(y_curr.shape) == 0:
y_curr = y_curr.unsqueeze(0)
margin_min_curr = margin_min[idx_to_fool]
loss_min_curr = loss_min[idx_to_fool]
p = self.p_selection(i_iter)
s = max(int(round(math.sqrt(p * n_features / c))), 1)
s = min(s, min(h, w))
vh = self.random_int(0, h - s)
vw = self.random_int(0, w - s)
new_deltas = torch.zeros([c, h, w]).to(self.device)
new_deltas[:, vh:vh + s, vw:vw + s
] = 2. * self.eps * self.random_choice([c, 1, 1])
x_new = x_best_curr + new_deltas
x_new = torch.min(torch.max(x_new, x_curr - self.eps),
x_curr + self.eps)
x_new = torch.clamp(x_new, 0., 1.)
x_new = self.check_shape(x_new)
margin, loss = self.margin_and_loss(x_new, y_curr)
# update loss if new loss is better
idx_improved = (loss < loss_min_curr).float()
loss_min[idx_to_fool] = idx_improved * loss + (
1. - idx_improved) * loss_min_curr
# update margin and x_best if new loss is better
# or misclassification
idx_miscl = (margin <= 0.).float()
idx_improved = torch.max(idx_improved, idx_miscl)
margin_min[idx_to_fool] = idx_improved * margin + (
1. - idx_improved) * margin_min_curr
idx_improved = idx_improved.reshape([-1,
*[1]*len(x.shape[:-1])])
x_best[idx_to_fool] = idx_improved * x_new + (
1. - idx_improved) * x_best_curr
n_queries[idx_to_fool] += 1.
ind_succ = (margin_min <= 0.).nonzero().squeeze()
if self.verbose and ind_succ.numel() != 0:
print('{}'.format(i_iter + 1),
'- success rate={}/{} ({:.2%})'.format(
ind_succ.numel(), n_ex_total,
float(ind_succ.numel()) / n_ex_total),
'- avg # queries={:.1f}'.format(
n_queries[ind_succ].mean().item()),
'- med # queries={:.1f}'.format(
n_queries[ind_succ].median().item()),
'- loss={:.3f}'.format(loss_min.mean()))
if ind_succ.numel() == n_ex_total:
break
elif self.norm == 'L2':
delta_init = torch.zeros_like(x)
s = h // 5
sp_init = (h - s * 5) // 2
vh = sp_init + 0
for _ in range(h // s):
vw = sp_init + 0
for _ in range(w // s):
delta_init[:, :, vh:vh + s, vw:vw + s] += self.eta(
s).view(1, 1, s, s) * self.random_choice(
[x.shape[0], c, 1, 1])
vw += s
vh += s
x_best = torch.clamp(x + self.normalize(delta_init
) * self.eps, 0., 1.)
margin_min, loss_min = self.margin_and_loss(x_best, y)
n_queries = torch.ones(x.shape[0]).to(self.device)
s_init = int(math.sqrt(self.p_init * n_features / c))
if (margin_min < 0.0).all():
return n_queries, x_best
for i_iter in range(self.n_queries):
idx_to_fool = (margin_min > 0.0).nonzero().squeeze()
x_curr = self.check_shape(x[idx_to_fool])
x_best_curr = self.check_shape(x_best[idx_to_fool])
y_curr = y[idx_to_fool]
if len(y_curr.shape) == 0:
y_curr = y_curr.unsqueeze(0)
margin_min_curr = margin_min[idx_to_fool]
loss_min_curr = loss_min[idx_to_fool]
delta_curr = x_best_curr - x_curr
p = self.p_selection(i_iter)
s = max(int(round(math.sqrt(p * n_features / c))), 3)
if s % 2 == 0:
s += 1
s = min(s, min(h, w))
vh = self.random_int(0, h - s)
vw = self.random_int(0, w - s)
new_deltas_mask = torch.zeros_like(x_curr)
new_deltas_mask[:, :, vh:vh + s, vw:vw + s] = 1.0
norms_window_1 = (delta_curr[:, :, vh:vh + s, vw:vw + s
] ** 2).sum(dim=(-2, -1), keepdim=True).sqrt()
vh2 = self.random_int(0, h - s)
vw2 = self.random_int(0, w - s)
new_deltas_mask_2 = torch.zeros_like(x_curr)
new_deltas_mask_2[:, :, vh2:vh2 + s, vw2:vw2 + s] = 1.
norms_image = self.lp_norm(x_best_curr - x_curr)
mask_image = torch.max(new_deltas_mask, new_deltas_mask_2)
norms_windows = ((delta_curr * mask_image) ** 2).sum(dim=(
-2, -1), keepdim=True).sqrt()
new_deltas = torch.ones([x_curr.shape[0], c, s, s]
).to(self.device)
new_deltas *= (self.eta(s).view(1, 1, s, s) *
self.random_choice([x_curr.shape[0], c, 1, 1]))
old_deltas = delta_curr[:, :, vh:vh + s, vw:vw + s] / (
1e-12 + norms_window_1)
new_deltas += old_deltas
new_deltas = new_deltas / (1e-12 + (new_deltas ** 2).sum(
dim=(-2, -1), keepdim=True).sqrt()) * (torch.max(
(self.eps * torch.ones_like(new_deltas)) ** 2 -
norms_image ** 2, torch.zeros_like(new_deltas)) /
c + norms_windows ** 2).sqrt()
delta_curr[:, :, vh2:vh2 + s, vw2:vw2 + s] = 0.
delta_curr[:, :, vh:vh + s, vw:vw + s] = new_deltas + 0
x_new = torch.clamp(x_curr + self.normalize(delta_curr
) * self.eps, 0. ,1.)
x_new = self.check_shape(x_new)
norms_image = self.lp_norm(x_new - x_curr)
margin, loss = self.margin_and_loss(x_new, y_curr)
# update loss if new loss is better
idx_improved = (loss < loss_min_curr).float()
loss_min[idx_to_fool] = idx_improved * loss + (
1. - idx_improved) * loss_min_curr
# update margin and x_best if new loss is better
# or misclassification
idx_miscl = (margin <= 0.).float()
idx_improved = torch.max(idx_improved, idx_miscl)
margin_min[idx_to_fool] = idx_improved * margin + (
1. - idx_improved) * margin_min_curr
idx_improved = idx_improved.reshape([-1,
*[1]*len(x.shape[:-1])])
x_best[idx_to_fool] = idx_improved * x_new + (
1. - idx_improved) * x_best_curr
n_queries[idx_to_fool] += 1.
ind_succ = (margin_min <= 0.).nonzero().squeeze()
if self.verbose and ind_succ.numel() != 0:
print('{}'.format(i_iter + 1),
'- success rate={}/{} ({:.2%})'.format(
ind_succ.numel(), n_ex_total, float(
ind_succ.numel()) / n_ex_total),
'- avg # queries={:.1f}'.format(
n_queries[ind_succ].mean().item()),
'- med # queries={:.1f}'.format(
n_queries[ind_succ].median().item()),
'- loss={:.3f}'.format(loss_min.mean()))
assert (x_new != x_new).sum() == 0
assert (x_best != x_best).sum() == 0
if ind_succ.numel() == n_ex_total:
break
elif self.norm == 'L1':
delta_init = torch.zeros_like(x)
s = h // 5
sp_init = (h - s * 5) // 2
vh = sp_init + 0
for _ in range(h // s):
vw = sp_init + 0
for _ in range(w // s):
delta_init[:, :, vh:vh + s, vw:vw + s] += self.eta(
s).view(1, 1, s, s) * self.random_choice(
[x.shape[0], c, 1, 1])
vw += s
vh += s
#x_best = torch.clamp(x + self.normalize(delta_init
# ) * self.eps, 0., 1.)
r_best = L1_projection(x, delta_init, self.eps * (1. - 1e-6))
x_best = x + delta_init + r_best
margin_min, loss_min = self.margin_and_loss(x_best, y)
n_queries = torch.ones(x.shape[0]).to(self.device)
s_init = int(math.sqrt(self.p_init * n_features / c))
if (margin_min < 0.0).all():
return n_queries, x_best
for i_iter in range(self.n_queries):
idx_to_fool = (margin_min > 0.0).nonzero().squeeze()
x_curr = self.check_shape(x[idx_to_fool])
x_best_curr = self.check_shape(x_best[idx_to_fool])
y_curr = y[idx_to_fool]
if len(y_curr.shape) == 0:
y_curr = y_curr.unsqueeze(0)
margin_min_curr = margin_min[idx_to_fool]
loss_min_curr = loss_min[idx_to_fool]
delta_curr = x_best_curr - x_curr
p = self.p_selection(i_iter)
s = max(int(round(math.sqrt(p * n_features / c))), 3)
if s % 2 == 0:
s += 1
#pass
s = min(s, min(h, w))
vh = self.random_int(0, h - s)
vw = self.random_int(0, w - s)
new_deltas_mask = torch.zeros_like(x_curr)
new_deltas_mask[:, :, vh:vh + s, vw:vw + s] = 1.0
norms_window_1 = delta_curr[:, :, vh:vh + s, vw:vw + s
].abs().sum(dim=(-2, -1), keepdim=True)
vh2 = self.random_int(0, h - s)
vw2 = self.random_int(0, w - s)
new_deltas_mask_2 = torch.zeros_like(x_curr)
new_deltas_mask_2[:, :, vh2:vh2 + s, vw2:vw2 + s] = 1.
norms_image = self.lp_norm(x_best_curr - x_curr)
mask_image = torch.max(new_deltas_mask, new_deltas_mask_2)
norms_windows = (delta_curr * mask_image).abs().sum(dim=(
-2, -1), keepdim=True)
new_deltas = torch.ones([x_curr.shape[0], c, s, s]
).to(self.device)
new_deltas *= (self.eta(s).view(1, 1, s, s) *
self.random_choice([x_curr.shape[0], c, 1, 1]))
old_deltas = delta_curr[:, :, vh:vh + s, vw:vw + s] / (
1e-12 + norms_window_1)
new_deltas += old_deltas
new_deltas = new_deltas / (1e-12 + new_deltas.abs().sum(
dim=(-2, -1), keepdim=True)) * (torch.max(
self.eps * torch.ones_like(norms_image) -
norms_image, torch.zeros_like(norms_image)) /
c + norms_windows) * c
delta_curr[:, :, vh2:vh2 + s, vw2:vw2 + s] = 0.
delta_curr[:, :, vh:vh + s, vw:vw + s] = new_deltas + 0
#
#norms_image_old = self.lp_norm(delta_curr)
r_curr = L1_projection(x_curr, delta_curr, self.eps * (1. - 1e-6))
x_new = x_curr + delta_curr + r_curr
x_new = self.check_shape(x_new)
norms_image = self.lp_norm(x_new - x_curr)
margin, loss = self.margin_and_loss(x_new, y_curr)
# update loss if new loss is better
idx_improved = (loss < loss_min_curr).float()
loss_min[idx_to_fool] = idx_improved * loss + (
1. - idx_improved) * loss_min_curr
# update margin and x_best if new loss is better
# or misclassification
idx_miscl = (margin <= 0.).float()
idx_improved = torch.max(idx_improved, idx_miscl)
margin_min[idx_to_fool] = idx_improved * margin + (
1. - idx_improved) * margin_min_curr
idx_improved = idx_improved.reshape([-1,
*[1]*len(x.shape[:-1])])
x_best[idx_to_fool] = idx_improved * x_new + (
1. - idx_improved) * x_best_curr
n_queries[idx_to_fool] += 1.
ind_succ = (margin_min <= 0.).nonzero().squeeze()
if self.verbose and ind_succ.numel() != 0:
print('{}'.format(i_iter + 1),
'- success rate={}/{} ({:.2%})'.format(
ind_succ.numel(), n_ex_total, float(
ind_succ.numel()) / n_ex_total),
'- avg # queries={:.1f}'.format(
n_queries[ind_succ].mean().item()),
'- med # queries={:.1f}'.format(
n_queries[ind_succ].median().item()),
'- loss={:.3f}'.format(loss_min.mean()),
'- max pert={:.3f}'.format(norms_image.max().item()),
#'- old pert={:.3f}'.format(norms_image_old.max().item())
)
assert (x_new != x_new).sum() == 0
assert (x_best != x_best).sum() == 0
if ind_succ.numel() == n_ex_total:
break
return n_queries, x_best
def perturb(self, x, y=None):
"""
:param x: clean images
:param y: untargeted attack -> clean labels,
if None we use the predicted labels
targeted attack -> target labels, if None random classes,
different from the predicted ones, are sampled
"""
self.init_hyperparam(x)
adv = x.clone()
#adv_all = x.clone()
if y is None:
if not self.targeted:
with torch.no_grad():
output = self.predict(x)
y_pred = output.max(1)[1]
y = y_pred.detach().clone().long().to(self.device)
else:
with torch.no_grad():
output = self.predict(x)
n_classes = output.shape[-1]
y_pred = output.max(1)[1]
y = self.random_target_classes(y_pred, n_classes)
else:
y = y.detach().clone().long().to(self.device)
if not self.targeted:
acc = self.predict(x).max(1)[1] == y
else:
acc = self.predict(x).max(1)[1] != y
startt = time.time()
torch.random.manual_seed(self.seed)
torch.cuda.random.manual_seed(self.seed)
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 = x[ind_to_fool].clone()
y_to_fool = y[ind_to_fool].clone()
_, adv_curr = self.attack_single_run(x_to_fool, y_to_fool)
output_curr = self.predict(adv_curr)
if not self.targeted:
acc_curr = output_curr.max(1)[1] == y_to_fool
else:
acc_curr = output_curr.max(1)[1] != y_to_fool
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()
#adv_all[ind_to_fool] = adv_curr.clone()
if self.verbose:
print('restart {} - robust accuracy: {:.2%}'.format(
counter, acc.float().mean()),
'- cum. time: {:.1f} s'.format(
time.time() - startt))
if not self.return_all:
return adv
else:
print('returning final points')
return adv_all
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