def _run(self, x, y, x_init=None):
self.f_model.reset()
if self.y_target is None:
criterion = fb.criteria.Misclassification(
as_tensor(y.ravel().astype('int64')))
else:
criterion = fb.criteria.TargetedMisclassification(
torch.tensor([self.y_target]))
x_t = as_tensor(x, requires_grad=False)
advx, clipped, is_adv = self.attack(self.f_model,
x_t,
criterion,
epsilons=self.epsilon)
if isinstance(clipped, list):
if len(clipped) == 1:
clipped = x[0]
else:
raise ValueError("This attack is returning a list. Please,"
"use a single value of epsilon.")
# f_opt is computed only in class-specific wrappers
f_opt = NaN
self._last_f_eval = self.f_model.f_eval
self._last_grad_eval = self.f_model.grad_eval
path = self.f_model.x_path
self._x_seq = CArray(path.numpy())
# reset again to clean cached data
self.f_model.reset()
return as_carray(clipped), f_opt
def _run(self, x, y, x_init=None):
self._x0 = as_tensor(x)
self._y0 = as_tensor(y)
out, _ = super(CFoolboxBasicIterative, self)._run(x, y, x_init)
self._f_seq = self.objective_function(self.x_seq)
f_opt = self.objective_function(out)
return out, f_opt
def _run(self, x, y, x_init=None):
self._x0 = as_tensor(x)
self._y0 = as_tensor(y)
out, _ = super(CFoolboxL2CarliniWagner, self)._run(x, y, x_init)
self._consts = self.attack.consts
self._f_seq = self.objective_function(self.x_seq)
self.best_c_ = self._consts[self.attack._best_const]
f_opt = self.objective_function(out)
return out, f_opt
def _check_adv_example(self, secml_attack, fb_attack):
x0_tensor = as_tensor(self.x0.atleast_2d())
y0_tensor = as_tensor(self.y0.ravel())
y_target = secml_attack.y_target
if y_target is None:
criterion = fb.criteria.Misclassification(y0_tensor)
else:
criterion = fb.criteria.TargetedMisclassification(torch.tensor([y_target]))
y_pred, scores, adv_ds, f_obj = secml_attack.run(self.x0, self.y0)
_, adv_fb, _ = fb_attack(secml_attack.f_model, x0_tensor, criterion, epsilons=secml_attack.epsilon)
adv_fb = CArray(adv_fb.numpy())
return adv_ds, adv_fb
def objective_function_gradient(self, x):
x_t = as_tensor(x).detach()
x_t.requires_grad_()
loss = self._adv_objective_function(x_t)
loss.sum().backward()
gradient = x_t.grad
return as_carray(gradient)
def _run(self, x, y, x_init=None):
self._x0 = as_tensor(x)
self._y0 = as_tensor(y)
out, _ = super(CFoolboxDeepfool, self)._run(x, y, x_init)
# fix the shape of the x_seq path in order to have
# always the same length as the number of steps
num_effective_steps = self.x_seq.shape[0]
if num_effective_steps < self.attack.steps:
added_vals = CArray.zeros((self.attack.steps - num_effective_steps,
*self.x_seq.shape[1:]))
added_vals += self.x_seq[-1, :]
self._x_seq = self._x_seq.append(added_vals, axis=0)
self.num_effective_steps = num_effective_steps # keep in case we need it
self._f_seq = self.objective_function(self.x_seq)
f_opt = self.objective_function(out)
return out, f_opt
def _run(self, x, y, x_init=None):
self._y0 = as_tensor(y)
out, _ = super(CFoolboxFGM, self)._run(x, y, x_init)
self._f_seq = self.objective_function(out)
f_opt = self._f_seq[-1]
# add last point of the path
self._x_seq = self._x_seq.append(out, axis=0)
return out, f_opt
def objective_function_gradient(self, x):
"""
Deepfool uses the gradient to find the closest class.
For this reason, if we need the gradient, the function
attempts to run the backward twice, which can be avoided
if we take care of saving the gradient at the first
pass.
"""
x_t = as_tensor(x).detach()
x_t.requires_grad_()
loss, gradient = self._adv_objective_function(x_t)
return as_carray(gradient)
def _adv_objective_function(self, x):
if self._x0 is None:
raise Exception('Attack not run yet')
l2dist = torch.norm(self._x0 - x.flatten(start_dim=1), dim=1, p=2)**2
loss = super(CWLoss, self)._adv_objective_function(x)
if x.shape[0] == self._consts.shape[0]:
c = as_tensor(self._consts)
else:
c = self._consts[-1].item()
total_loss = c * loss + l2dist
return total_loss
def _run(self, x, y, x_init=None):
self._y0 = as_tensor(y)
out, _ = super(CFoolboxFGM, self)._run(x, y, x_init)
self._f_seq = self.objective_function(out)
f_opt = self._f_seq[-1]
return out, f_opt
def objective_function(self, x):
"""
Accordingly, we should also return only the first
returned value of the function.
"""
return as_carray(self._adv_objective_function(as_tensor(x))[0])
def objective_function(self, x):
return as_carray(self._adv_objective_function(as_tensor(x)))
