diff --git a/Filter_Analysis/New_Snapped_Performace.png b/Filter_Analysis/New_Snapped_Performace.png
new file mode 100644
index 0000000..dd4ec8f
Binary files /dev/null and b/Filter_Analysis/New_Snapped_Performace.png differ
diff --git a/Filter_Analysis/fgsm.py b/Filter_Analysis/fgsm.py
index 75a82d2..cf1dc54 100644
--- a/Filter_Analysis/fgsm.py
+++ b/Filter_Analysis/fgsm.py
@@ -9,7 +9,7 @@ import cv2
 from mnist import Net
 from pykuwahara import kuwahara
 
-epsilons = np.arange(0.05,0.35,0.05)
+epsilons = np.arange(0.0,0.35,0.05)
 pretrained_model = "mnist_cnn_unfiltered.pt"
 use_cuda=False
 
@@ -211,8 +211,23 @@ def filtered(data, batch_size=64, filter="kuwahara"):
         for i in range(batch_size):
             filtered_images[i] = cv2.bilateralFilter(images[i], 5, 50, 50).reshape(filtered_images[i].shape)
     elif filter == "snap_color":
+        num_colors = 2
         for i in range(batch_size):
-            filtered_images[i] = (images[i]*4).astype(int).astype(float) / 4
+            # If the channel contains any negative values, define the lowest negative value as black
+            min_value = np.min(images[i])
+            if (min_value < 0):
+                filtered_images[i] = images[i] + min_value
+
+            # If the color space extends beyond [0,1], re-scale all of the colors to that range
+            max_value = np.max(filtered_images[i])
+            if (max_value > 1):
+                filtered_images[i] *= (num_colors/max_value)
+                filtered_images[i] = filtered_images[i].astype(int).astype(float)*(max_value/num_colors)
+            else:
+                filtered_images[i] *= num_colors
+                filtered_images[i] = filtered_images[i].astype(int).astype(float)/num_colors
+            if (min_value < 0):
+                filtered_images[i] -= min_value
 
     # Modify the data with the filtered image
     filtered_images = filtered_images.transpose(0,3,1,2)