def __init__(self,
pgd_alpha: float = 2.0 / 255,
pgd_eps: float = 8.0 / 255,
pgd_iter: int = 7,
**kwargs):
super().__init__(**kwargs)
self.param_list['adv_train'] = ['pgd_alpha', 'pgd_eps', 'pgd_iter']
self.pgd_alpha = pgd_alpha
self.pgd_eps = pgd_eps
self.pgd_iter = pgd_iter
self.pgd = PGD(pgd_alpha=pgd_alpha,
pgd_eps=pgd_eps,
iteration=pgd_iter,
stop_threshold=None,
target_idx=0,
model=self.model,
dataset=self.dataset)
def attack(self, epochs: int, **kwargs):
# model._validate()
total = 0
target_conf_list = []
target_acc_list = []
clean_acc_list = []
pgd_norm_list = []
pgd_alpha = 1.0 / 255
pgd_eps = 8.0 / 255
if self.dataset.name in ['cifar10', 'gtsrb', 'isic2018']:
pgd_alpha = 1.0 / 255
pgd_eps = 8.0 / 255
if self.dataset.name in ['sample_imagenet', 'sample_vggface2']:
pgd_alpha = 0.25 / 255
pgd_eps = 2.0 / 255
pgd_checker = PGD(pgd_alpha=pgd_alpha, pgd_eps=pgd_eps, iteration=8,
dataset=self.dataset, model=self.model, target_idx=self.target_idx, stop_threshold=0.95)
easy = 0
difficult = 0
normal = 0
loader = self.dataset.get_dataloader(mode='valid', batch_size=1)
if 'curvature' in self.__dict__.keys():
benign_curvature = self.curvature.benign_measure()
tgt_curvature_list = []
org_curvature_list = []
if self.randomized_smooth:
org_conf_list = []
tgt_conf_list = []
if 'magnet' in self.__dict__.keys():
org_magnet_list = []
tgt_magnet_list = []
for data in loader:
print(easy, normal, difficult)
if normal >= 100:
break
self.model.load()
_input, _label = self.model.remove_misclassify(data)
if len(_label) == 0:
continue
target_label = self.model.generate_target(_input, idx=self.target_idx)
self.temp_input = _input
self.temp_label = target_label
_, _iter = pgd_checker.optimize(_input)
if _iter is None:
difficult += 1
continue
if _iter < 4:
easy += 1
continue
normal += 1
target_conf, target_acc, clean_acc = self.validate_fn()
noise = torch.zeros_like(_input)
trigger_input = self.optimize(_input=_input, _label=target_label, epochs=epochs, noise=noise, **kwargs)
pgd_norm = float(noise.norm(p=float('inf')))
target_conf, target_acc, clean_acc = self.validate_fn()
target_conf_list.append(target_conf)
target_acc_list.append(target_acc)
clean_acc_list.append(max(self.clean_acc - clean_acc, 0.0))
pgd_norm_list.append(pgd_norm)
print(f'[{total+1} / 100]\n'
f'target confidence: {np.mean(target_conf_list)}({np.std(target_conf_list)})\n'
f'target accuracy: {np.mean(target_acc_list)}({np.std(target_acc_list)})\n'
f'clean accuracy Drop: {np.mean(clean_acc_list)}({np.std(clean_acc_list)})\n'
f'PGD Norm: {np.mean(pgd_norm_list)}({np.std(pgd_norm_list)})\n\n\n')
org_conf = self.model.get_target_prob(_input=trigger_input, target=_label)
tgt_conf = self.model.get_target_prob(_input=trigger_input, target=target_label)
if 'curvature' in self.__dict__.keys():
org_curvature_list.extend(self.curvature.measure(trigger_input, _label).detach().cpu().tolist())
tgt_curvature_list.extend(self.curvature.measure(trigger_input, target_label).detach().cpu().tolist())
print('Curvature:')
print(f' org_curvature: {ks_2samp(org_curvature_list, benign_curvature)}') # type: ignore
print(f' tgt_curvature: {ks_2samp(tgt_curvature_list, benign_curvature)}') # type: ignore
print()
if self.randomized_smooth:
org_new = self.model.get_target_prob(_input=trigger_input, target=_label, randomized_smooth=True)
tgt_new = self.model.get_target_prob(_input=trigger_input, target=target_label, randomized_smooth=True)
org_increase = (org_new - org_conf).clamp(min=0.0)
tgt_decrease = (tgt_new - tgt_conf).clamp(min=0.0)
org_conf_list.extend(org_increase.detach().cpu().tolist())
tgt_conf_list.extend(tgt_decrease.detach().cpu().tolist())
print('Randomized Smooth:')
print(f' org_confidence: {np.mean(org_conf_list)}')
print(f' tgt_confidence: {np.mean(tgt_conf_list)}')
print()
if 'magnet' in self.__dict__.keys():
trigger_input = self.magnet(trigger_input)
org_new = self.model.get_target_prob(_input=trigger_input, target=_label)
tgt_new = self.model.get_target_prob(_input=trigger_input, target=target_label)
org_increase = (org_new - org_conf).clamp(min=0.0)
tgt_decrease = (tgt_conf - tgt_new).clamp(min=0.0)
org_magnet_list.extend(org_increase.detach().cpu().tolist())
tgt_magnet_list.extend(tgt_decrease.detach().cpu().tolist())
print('MagNet:')
print(f' org_confidence: {np.mean(org_magnet_list)}')
print(f' tgt_confidence: {np.mean(tgt_magnet_list)}')
print()
total += 1