Implement obfuscation.

main.py

@@ -234,7 +234,7 @@ if __name__ == "__main__":
                     pass
 
                 # iou, pred = get_best_prediction(bounding_box, raw_results, 15)  # get cats
-                iou, pred = get_best_prediction(bounding_box, raw_results, 12)  # get parking meters
+                # iou, pred = get_best_prediction(bounding_box, raw_results, 12)  # get parking meters
                 iou, pred = get_best_prediction(bounding_box, raw_results, 11)  # get stop signs
                 # pred = get_best_prediction(bounding_box, raw_results, 42)  # get forked
 

patch_transformer.py

@@ -20,7 +20,7 @@ class PatchApplier(nn.Module):
         super(PatchApplier, self).__init__()
 
     def forward(self, img, patch):
-        img = torch.where(patch < 1e-05, img/256, patch) * 256
+        img = torch.where(patch < 1e-05, img / 256, patch) * 256
         return img
 
 
@@ -70,6 +70,7 @@ class MedianPool2d(nn.Module):
         x = x.contiguous().view(x.size()[:4] + (-1,)).median(dim=-1)[0]
         return x
 
+
 class PatchTransformer(nn.Module):
     """PatchTransformer: transforms batch of patches
 
@@ -101,7 +102,19 @@ class PatchTransformer(nn.Module):
         '''
 
     def forward(self, adv_patch, lab_batch, img_size_x, img_size_y,
-                do_rotate=True, rand_loc=True, rand_size=True, gauss=True, max_sigma=6, rand_squeeze=True):
+                do_rotate=True, rand_loc=True, rand_size=True, gauss=True, max_sigma=6,
+                obfuscate=True, max_obs_part=1, rand_squeeze=True):
+
+        # Obfuscation
+        if obfuscate:
+            max_x = adv_patch.shape[1]
+            max_y = adv_patch.shape[2]
+            obfuscation_root = [int(random.random() * max_x), int(random.random() * max_y)]
+            obfuscation_shape = [int(random.random() * max_obs_part * max_x),
+                                 int(random.random() * max_obs_part * max_y)]
+            adv_patch[:, obfuscation_root[0]:obfuscation_root[0] + obfuscation_shape[0],
+            obfuscation_root[1]:obfuscation_root[1] + obfuscation_shape[1]] = 0.001
+
         # adv_patch = F.conv2d(adv_patch.unsqueeze(0),self.kernel,padding=(2,2))
         adv_patch = self.medianpooler(adv_patch.unsqueeze(0))
         # Determine size of padding
@@ -131,7 +144,7 @@ class PatchTransformer(nn.Module):
         noise = torch.cuda.FloatTensor(adv_batch.size()).uniform_(-1, 1) * self.noise_factor
 
         # Apply contrast/brightness/noise, clamp
-        adv_batch = adv_batch # * contrast + brightness + noise
+        adv_batch = adv_batch  # * contrast + brightness + noise
 
         adv_batch = torch.clamp(adv_batch, 0.000001, 0.99999)
 
@@ -194,8 +207,6 @@ class PatchTransformer(nn.Module):
         sin = torch.sin(angle)
         cos = torch.cos(angle)
 
-
-
         # Theta = rotation,rescale matrix
         theta = torch.cuda.FloatTensor(anglesize, 2, 3).fill_(0)
         theta[:, 0, 0] = cos / scale_x
@@ -242,7 +253,7 @@ class PatchTransformer(nn.Module):
         if gauss:
             adv_batch_t = self.gauss_filter(adv_batch_t, random.random() * max_sigma)
 
-        return adv_batch_t #* msk_batch_t
+        return adv_batch_t  # * msk_batch_t
 
     def gauss_filter(self, img, sigma):