def attack(self, model, xo, untargeted, target, w, loss_function=ai.stdLoss, **kargs):
w = self.epsilon.getVal(c = w, **kargs)
x = nn.Parameter(xo.clone(), requires_grad=True)
gradorg = h.zeros(x.shape)
is_eq = 1
w = h.ones(x.shape) * w
for i in range(self.k):
if self.restart is not None and i % int(self.k / self.restart) == 0:
x = is_eq * (torch.rand_like(xo) * w + xo) + (1 - is_eq) * x
x = nn.Parameter(x, requires_grad = True)
model.optimizer.zero_grad()
out = model(x).vanillaTensorPart()
loss = loss_function(out, target)
loss.sum().backward(retain_graph=True)
with torch.no_grad():
oth = x.grad / torch.norm(x.grad, p=1)
gradorg *= self.mu
gradorg += oth
grad = (self.r * w / self.k) * ai.mysign(gradorg)
if self.should_end:
is_eq = ai.mulIfEq(grad, out, target)
x = (x + grad * is_eq) if untargeted else (x - grad * is_eq)
x = xo + torch.min(torch.max(x - xo, -w),w)
x.requires_grad_()
model.optimizer.zero_grad()
return x
def attack(self,
model,
epsilon,
xo,
untargeted,
target,
loss_function=ai.stdLoss):
if not self.epsilon is None:
epsilon = self.epsilon
x = nn.Parameter(xo.clone(), requires_grad=True)
gradorg = h.zeros(x.shape)
is_eq = 1
for i in range(self.k):
if self.restart is not None and i % int(
self.k / self.restart) == 0:
x = is_eq * (torch.randn_like(xo) * epsilon + xo) + (1 -
is_eq) * x
x = nn.Parameter(x, requires_grad=True)
model.optimizer.zero_grad()
out = model(x)
loss = loss_function(out, target)
loss.backward()
with torch.no_grad():
oth = x.grad / torch.norm(x.grad, p=1)
gradorg *= self.mu
gradorg += oth
grad = (self.r * epsilon / self.k) * ai.mysign(gradorg)
if self.should_end:
is_eq = ai.mulIfEq(grad, out, target)
x = (x + grad * is_eq) if untargeted else (x - grad * is_eq)
x = xo + torch.clamp(x - xo, -epsilon, epsilon)
x.requires_grad_()
model.optimizer.zero_grad()
return x