def perturb(self, x, y=None):
"""
Given examples (x, y), returns their adversarial counterparts with
an attack length of eps.
:param x: input tensor.
:param y: label tensor.
- if None and self.targeted=False, compute y as predicted
labels.
- if self.targeted=True, then y must be the targeted labels.
:return: tensor containing perturbed inputs.
"""
x, y = self._verify_and_process_inputs(x, y)
delta = torch.zeros_like(x)
delta = nn.Parameter(delta)
if self.rand_init:
rand_init_delta(
delta, x, self.ord, self.eps, self.clip_min, self.clip_max)
delta.data = clamp(
x + delta.data, min=self.clip_min, max=self.clip_max) - x
rval = perturb_iterative(
x, y, self.predict, nb_iter=self.nb_iter,
eps=self.eps, eps_iter=self.eps_iter,
loss_fn=self.loss_fn, minimize=self.targeted,
ord=self.ord, clip_min=self.clip_min,
clip_max=self.clip_max, delta_init=delta,
l1_sparsity=self.l1_sparsity, accumulate_param_grad_prob=self.accumulate_param_grad_prob
)
return rval.data
def perturb_fool_many(self,
x,
emb,
indlist,
y=None): #list of ind of words to be perturbed
"""
Given examples (x, y), returns their adversarial counterparts with
an attack length of eps.
:param x: input tensor.
:param y: label tensor.
- if None and self.targeted=False, compute y as predicted
labels.
- if self.targeted=True, then y must be the targeted labels.
:return: tensor containing perturbed inputs.
"""
emb, y = self._verify_and_process_inputs(emb, y) #???
delta = torch.zeros_like(emb)
delta = nn.Parameter(delta)
if self.rand_init:
rand_init_delta(delta, emb, np.inf, self.eps, self.clip_min,
self.clip_max)
delta.data = clamp(emb + delta.data,
min=self.clip_min,
max=self.clip_max) - emb
with torch.no_grad():
for ba in range(delta.size()[0]):
for t in range(delta.size()[1]):
if not (t in indlist[ba]):
for k in range(delta.size()[2]):
delta[ba][t][k] = 0
if self.ord == 0:
for ba in range(delta.size()[0]):
delta[ba] = my_proj_all(emb[ba] + delta[ba], emb[ba],
indlist[ba],
self.eps) - emb[ba]
rval, word_balance_memory, loss_memory, tablistbatch, fool = perturb_iterative_fool_many(
x,
emb,
indlist,
y,
self.predict,
nb_iter=self.nb_iter,
eps=self.eps,
epscand=self.epscand,
eps_iter=self.eps_iter,
loss_fn=self.loss_fn,
minimize=self.targeted,
ord=self.ord,
clip_min=self.clip_min,
clip_max=self.clip_max,
delta_init=delta,
l1_sparsity=self.l1_sparsity,
rayon=self.rayon)
return rval.data, word_balance_memory, loss_memory, tablistbatch, fool
def perturb(self, x, y=None):
"""
Given examples (x, y), returns their adversarial counterparts with
an attack length of eps.
:param x: input tensor.
:param y: label tensor.
- if None and self.targeted=False, compute y as predicted
labels.
- if self.targeted=True, then y must be the targeted labels.
:return: tensor containing perturbed inputs.
"""
x, y = self._verify_and_process_inputs(x, y)
shape, flat_x = _flatten(x)
data_shape = tuple(shape[1:])
def f(x):
new_shape = (x.shape[0], ) + data_shape
input = x.reshape(new_shape)
return self.predict(input)
f_nes = NESWrapper(f, nb_samples=self.nb_samples, fd_eta=self.fd_eta)
delta = torch.zeros_like(flat_x)
delta = nn.Parameter(delta)
if self.rand_init:
rand_init_delta(delta, flat_x, self.ord, self.eps, self.clip_min,
self.clip_max)
delta.data = clamp(flat_x + delta.data,
min=self.clip_min,
max=self.clip_max) - flat_x
rval = perturb_iterative(flat_x,
y,
f_nes,
nb_iter=self.nb_iter,
eps=self.eps,
eps_iter=self.eps_iter,
loss_fn=self.loss_fn,
minimize=self.targeted,
ord=self.ord,
clip_min=self.clip_min,
clip_max=self.clip_max,
delta_init=delta,
l1_sparsity=None)
return rval.data.reshape(shape)
def perturb(self, x, y=None, num_rand_init=3):
max_loss = -10000
max_adv_x = torch.zeros_like(x)
for rand_int in range(num_rand_init):
delta = torch.zeros_like(x)
delta = nn.Parameter(delta)
if self.rand_init:
rand_init_delta(
delta, x, self.ord, self.eps, self.clip_min, self.clip_max)
delta.data = clamp(
x + delta.data, min=self.clip_min, max=self.clip_max) - x
adv_x, adv_loss = perturb_iterative(
x, y,
predictor_list=self.estimator_list,
dis_list=self.distribution_list,
nb_iter=self.nb_iter,
eps=self.eps, eps_iter=self.eps_iter,
loss_fn=self.loss_fn, minimize=self.targeted,
ord=self.ord, clip_min=self.clip_min,
clip_max=self.clip_max, delta_init=delta, sparsity=self.sparsity)
if max_loss < adv_loss:
max_loss = adv_loss
max_adv_x = adv_x
return max_adv_x.data
def perturb_iterative(xvar,
yvar,
predict,
nb_iter,
eps,
eps_iter,
loss_fn,
delta_init=None,
minimize=False,
ord=np.inf,
clip_min=0.0,
clip_max=1.0,
sparsity=0.01):
"""
Iteratively maximize the loss over the input. It is a shared method for
iterative attacks including IterativeGradientSign, LinfPGD, etc.
:param xvar: input data.
:param yvar: input labels.
:param predict: forward pass function.
:param nb_iter: number of iterations.
:param eps: maximum distortion.
:param eps_iter: attack step size.
:param loss_fn: loss function.
:param delta_init: (optional) tensor contains the random initialization.
:param minimize: (optional bool) whether to minimize or maximize the loss.
:param ord: (optional) the order of maximum distortion (inf or 2).
:param clip_min: mininum value per input dimension.
:param clip_max: maximum value per input dimension.
:return: tensor containing the perturbed input.
"""
max_loss_value = -100000
for jj in range(3):
max_loss_value_iter = -100000
if delta_init is not None:
delta = torch.zeros_like(xvar)
delta = nn.Parameter(delta)
rand_init_delta(delta, xvar, ord, eps, clip_min, clip_max)
delta.data = clamp(xvar + delta.data, min=clip_min,
max=clip_max) - xvar
else:
delta = torch.zeros_like(xvar)
delta.requires_grad_()
for ii in range(nb_iter):
avg_grad = torch.FloatTensor(xvar.shape)
avg_grad.zero_()
if xvar.is_cuda:
avg_grad = avg_grad.cuda()
p = predict.weights / np.sum(predict.weights)
for id_classifier, classifier in enumerate(predict.classifiers):
if id_classifier == 0:
outputs = classifier(xvar + delta) * p[id_classifier]
else:
outputs = outputs + classifier(xvar +
delta) * p[id_classifier]
loss = loss_fn(outputs, yvar)
if minimize:
loss = -loss
loss.backward()
avg_grad = delta.grad.detach()
if ord == np.inf:
grad_sign = avg_grad.sign()
delta.data = delta.data + batch_multiply(eps_iter, grad_sign)
delta.data = batch_clamp(eps, delta.data)
delta.data = clamp(xvar.data + delta.data, clip_min,
clip_max) - xvar.data
elif ord == 2:
grad = avg_grad
grad = normalize_by_pnorm(grad)
delta.data = delta.data + batch_multiply(eps_iter, grad)
delta.data = clamp(xvar.data + delta.data, clip_min,
clip_max) - xvar.data
if eps is not None:
delta.data = clamp_by_pnorm(delta.data, ord, eps)
elif ord == 1:
with torch.no_grad():
grad = avg_grad
abs_grad = torch.abs(avg_grad)
batch_size = grad.size(0)
view = abs_grad.view(batch_size, -1)
view_size = view.size(1)
vals, idx = view.topk(int(sparsity * view_size))
out = torch.zeros_like(view).scatter_(1, idx, vals)
out = out.view_as(grad)
grad = grad.sign() * (out > 0).float()
grad = normalize_by_pnorm(grad, p=1)
delta.data += batch_multiply(eps_iter, grad)
delta.data = batch_l1_proj(delta.data.cpu(), eps)
if xvar.is_cuda:
delta.data = delta.data.cuda()
delta.data = clamp(xvar.data + delta.data, clip_min,
clip_max) - xvar.data
else:
error = "Only ord = inf, ord = 1 and ord = 2 have been implemented"
raise NotImplementedError(error)
delta.grad.data.zero_()
x_adv = clamp(xvar + delta, clip_min, clip_max)
for id_classifier, classifier in enumerate(predict.classifiers):
if id_classifier == 0:
outputs_2 = classifier(xvar + delta) * p[id_classifier]
else:
outputs_2 = outputs_2 + classifier(
xvar + delta) * p[id_classifier]
loss_2 = loss_fn(outputs, yvar)
if max_loss_value_iter < loss_2:
max_loss_value_iter = loss_2
max_adv_iter = x_adv
if max_loss_value < max_loss_value_iter:
max_loss_value = max_loss_value_iter
max_adv = max_adv_iter
return max_adv