| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| from __future__ import unicode_literals |
|
|
| import time |
| import math |
|
|
| import torch |
| import torch.nn.functional as F |
|
|
| from ..attack import Attack |
|
|
|
|
| class Square(Attack): |
| r""" |
| Square Attack in the paper 'Square Attack: a query-efficient black-box adversarial attack via random search' |
| [https://arxiv.org/abs/1912.00049] |
| [https://github.com/fra31/auto-attack] |
| |
| Distance Measure : Linf, L2 |
| |
| Arguments: |
| model (nn.Module): model to attack. |
| norm (str): Lp-norm of the attack. ['Linf', 'L2'] (Default: 'Linf') |
| eps (float): maximum perturbation. (Default: 8/255) |
| n_queries (int): max number of queries (each restart). (Default: 5000) |
| n_restarts (int): number of random restarts. (Default: 1) |
| p_init (float): parameter to control size of squares. (Default: 0.8) |
| loss (str): loss function optimized ['margin', 'ce'] (Default: 'margin') |
| resc_schedule (bool): adapt schedule of p to n_queries (Default: True) |
| seed (int): random seed for the starting point. (Default: 0) |
| verbose (bool): print progress. (Default: False) |
| targeted (bool): targeted. (Default: False) |
| |
| Shape: |
| - images: :math:`(N, C, H, W)` where `N = number of batches`, `C = number of channels`, `H = height` and `W = width`. It must have a range [0, 1]. |
| - labels: :math:`(N)` where each value :math:`y_i` is :math:`0 \leq y_i \leq` `number of labels`. |
| - output: :math:`(N, C, H, W)`. |
| |
| Examples:: |
| >>> attack = torchattacks.Square(model, model, norm='Linf', eps=8/255, n_queries=5000, n_restarts=1, eps=None, p_init=.8, seed=0, verbose=False, targeted=False, loss='margin', resc_schedule=True) |
| >>> adv_images = attack(images, labels) |
| |
| """ |
|
|
| def __init__( |
| self, |
| model, |
| norm="Linf", |
| eps=8 / 255, |
| n_queries=5000, |
| n_restarts=1, |
| p_init=0.8, |
| loss="margin", |
| resc_schedule=True, |
| seed=0, |
| verbose=False, |
| ): |
| super().__init__("Square", model) |
| 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.loss = loss |
| self.rescale_schedule = resc_schedule |
| self.supported_mode = ["default", "targeted"] |
|
|
| def forward(self, images, labels): |
| r""" |
| Overridden. |
| """ |
|
|
| images = images.clone().detach().to(self.device) |
| labels = labels.clone().detach().to(self.device) |
| adv_images = self.perturb(images, labels) |
|
|
| return adv_images |
|
|
| def margin_and_loss(self, x, y): |
| """ |
| :param y: correct labels if untargeted else target labels |
| """ |
|
|
| logits = self.get_logits(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, -1.0 * xent |
| elif self.loss == "margin": |
| return y_corr - y_others, y_corr - y_others |
| else: |
| if self.loss == "ce": |
| return y_others - y_corr, xent |
| elif self.loss == "margin": |
| return y_others - y_corr, y_others - y_corr |
|
|
| def init_hyperparam(self, x): |
| assert self.norm in ["Linf", "L2"] |
| 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 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_delta(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) |
|
|
| 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)) |
|
|
| 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] |
| 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() |
|
|
| return delta |
|
|
| def eta(self, s): |
| delta = torch.zeros([s, s]).to(self.device) |
| delta[: s // 2] = self.eta_rectangles(s // 2, s) |
| delta[s // 2 :] = -1.0 * self.eta_rectangles(s - s // 2, s) |
| delta /= (delta ** 2).sum(dim=(0, 1), keepdim=True).sqrt() |
| 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.norm == "Linf": |
| x_best = torch.clamp( |
| x + self.eps * self.random_choice([x.shape[0], c, 1, w]), 0.0, 1.0 |
| ) |
| 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)) |
|
|
| for i_iter in range(self.n_queries): |
| idx_to_fool = (margin_min > 0.0).nonzero().flatten() |
|
|
| if len(idx_to_fool) == 0: |
| break |
|
|
| 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) |
| 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] = ( |
| 2.0 * 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, 0.0, 1.0) |
| x_new = self.check_shape(x_new) |
|
|
| margin, loss = self.margin_and_loss(x_new, y_curr) |
|
|
| |
| idx_improved = (loss < loss_min_curr).float() |
|
|
| loss_min[idx_to_fool] = ( |
| idx_improved * loss + (1.0 - idx_improved) * loss_min_curr |
| ) |
|
|
| |
| |
| idx_miscl = (margin <= 0.0).float() |
| idx_improved = torch.max(idx_improved, idx_miscl) |
|
|
| margin_min[idx_to_fool] = ( |
| idx_improved * margin + (1.0 - 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 + (1.0 - idx_improved) * x_best_curr |
| ) |
| n_queries[idx_to_fool] += 1.0 |
|
|
| ind_succ = (margin_min <= 0.0).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(delta_init) * self.eps, 0.0, 1.0 |
| ) |
| 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)) |
|
|
| for i_iter in range(self.n_queries): |
| idx_to_fool = (margin_min > 0.0).nonzero().flatten() |
|
|
| if len(idx_to_fool) == 0: |
| break |
|
|
| 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 |
|
|
| 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] = 1.0 |
|
|
| norms_image = self.lp_norm(x_best_curr - x_curr) |
| mask_image = torch.max(new_deltas_mask, new_deltas_mask_2) |
| norms_windows = self.lp_norm(delta_curr * mask_image) |
|
|
| 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] = 0.0 |
| delta_curr[:, :, vh : vh + s, vw : vw + s] = new_deltas + 0 |
|
|
| x_new = torch.clamp( |
| x_curr + self.normalize_delta(delta_curr) * self.eps, 0.0, 1.0 |
| ) |
| 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) |
|
|
| |
| idx_improved = (loss < loss_min_curr).float() |
|
|
| loss_min[idx_to_fool] = ( |
| idx_improved * loss + (1.0 - idx_improved) * loss_min_curr |
| ) |
|
|
| |
| |
| idx_miscl = (margin <= 0.0).float() |
| idx_improved = torch.max(idx_improved, idx_miscl) |
|
|
| margin_min[idx_to_fool] = ( |
| idx_improved * margin + (1.0 - 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 + (1.0 - idx_improved) * x_best_curr |
| ) |
| n_queries[idx_to_fool] += 1.0 |
|
|
| ind_succ = (margin_min <= 0.0).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 |
|
|
| 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() |
| if y is None: |
| if not self.targeted: |
| with torch.no_grad(): |
| output = self.get_logits(x) |
| y_pred = output.max(1)[1] |
| y = y_pred.detach().clone().long().to(self.device) |
| else: |
| with torch.no_grad(): |
| y = self.get_target_label(x, None) |
| else: |
| if not self.targeted: |
| y = y.detach().clone().long().to(self.device) |
| else: |
| y = self.get_target_label(x, y) |
|
|
| if not self.targeted: |
| acc = self.get_logits(x).max(1)[1] == y |
| else: |
| acc = self.get_logits(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.get_logits(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() |
| if self.verbose: |
| print( |
| "restart {} - robust accuracy: {:.2%}".format( |
| counter, acc.float().mean() |
| ), |
| "- cum. time: {:.1f} s".format(time.time() - startt), |
| ) |
|
|
| return adv |
|
|
