diff --git a/Filter_Analysis/__pycache__/defense_filters.cpython-312.pyc b/Filter_Analysis/__pycache__/defense_filters.cpython-312.pyc
index 5e94778..f26c8e7 100644
Binary files a/Filter_Analysis/__pycache__/defense_filters.cpython-312.pyc and b/Filter_Analysis/__pycache__/defense_filters.cpython-312.pyc differ
diff --git a/Filter_Analysis/defense_filters.py b/Filter_Analysis/defense_filters.py
index 1fa318a..837ee8d 100644
--- a/Filter_Analysis/defense_filters.py
+++ b/Filter_Analysis/defense_filters.py
@@ -128,7 +128,7 @@ def filtered(data, batch_size=64, strength=0, filter="gaussian_blur"):
         bits = 2**strength
         return bit_depth(data, batch_size, bits)
     elif filter == "random_noise":
-        intensity == 0.0005*(2*strength + 1)
+        intensity = 0.0005*(2*strength + 1)
         return random_noise(data, batch_size, intensity)
     else:
         strength = (2*strength + 1)
diff --git a/Filter_Analysis/fgsm.py b/Filter_Analysis/fgsm.py
index 16fc7d0..2a16eb3 100644
--- a/Filter_Analysis/fgsm.py
+++ b/Filter_Analysis/fgsm.py
@@ -85,7 +85,7 @@ def test(model, device, test_loader, epsilon, filter):
     
     test_step = 0
     for data, target in test_loader:
-        print("[" + "="*int(20*test_step/len(test_loader)) + " "*(20 - int(20*test_step/len(test_loader))) + "]", f"{100*test_step/len(test_loader)}%", end='\r')
+        print(filter, f"Epsilon: {epsilon}", "[" + "="*int(1 + 20*test_step/len(test_loader)) + " "*(20 - int(20*test_step/len(test_loader))) + "]", f"{100*test_step/len(test_loader)}%", end='\r')
         test_step += 1
 
         data, target = data.to(device), target.to(device)
@@ -163,18 +163,23 @@ def test(model, device, test_loader, epsilon, filter):
     return unfiltered_acc, filtered_accuracies
 
 
-accurracies = {}
+accuracies = {}
 filters = ("gaussian_blur", "gaussian_kuwahara", "mean_kuwahara", "random_noise", "bilateral_filter", "bit_depth", "threshold_filter")
 
 for filter in filters:
     for eps in epsilons:
         unfiltered_accuracy, filtered_accuracy = test(model, device, test_loader, eps, filter)
 
-        if len(accuracies["unfiltered"] < len(epsilons):
+        if list(accuracies.keys()).count("unfiltered") == 0:
+            accuracies["unfiltered"] = []
+        if len(accuracies["unfiltered"]) < len(epsilons):
             accuracies["unfiltered"].append(unfiltered_accuracy)
+
+        if list(accuracies.keys()).count(filter) == 0:
+            accuracies[filter] = []
         accuracies[filter].append(filtered_accuracy)
 
-json.dump(accuracies, "fgsm_accuracies.json")
+json.dump(accuracies, "fgsm_mnist_accuracies.json")
 
 # Plot the results
 #plt.figure(figsize=(16,9))