next

cyclegan/models/test_model.py

@@ -77,7 +77,7 @@ class TestModel(BaseModel):
         pgd_attack = attacks.LinfPGDAttack(model=self.netG)
         black = np.zeros((1, 3, image.size(2), image.size(3)))
         black = torch.FloatTensor(black).cuda()
-        input_adv, perturb = pgd_attack.perturb(image, image)      
+        input_adv, perturb = pgd_attack.perturb(image, black)      
         
         return input_adv, perturb
 

cyclegan/util/attacks.py

@@ -7,7 +7,7 @@ import torch
 import torch.nn as nn
 
 class LinfPGDAttack(object):
-    def __init__(self, model=None, epsilon=0.15, k=40, a=0.01):
+    def __init__(self, model=None, epsilon=0.05, k=1, a=0.01):
         self.model = model
         self.epsilon = epsilon
         self.k = k

stargan/solver.py

@@ -609,8 +609,8 @@ class Solver(object):
             # Translate images.
             x_fake_list = [x_real]
 
-            # if i == 0:
-            #     x_adv, perturb = pgd_attack.perturb(x_real, x_real, c_trg_list[0])
+            if i == 0:
+                x_adv, perturb = pgd_attack.perturb(x_real, x_real, c_trg_list[0])
 
             for c_trg in c_trg_list:
                 with torch.no_grad():
@@ -639,6 +639,8 @@ class Solver(object):
                     l0_error += (gen - gen_noattack).norm(0)
                     min_dist += (gen - gen_noattack).norm(float('-inf'))
                     n_samples += 1
+                
+                break
 
             # Save the translated images.
             x_concat = torch.cat(x_fake_list, dim=3)