diff --git a/datasets/fake/generate_dataset.py b/datasets/fake/generate_dataset.py
index 3233cce..a3df0cd 100644
--- a/datasets/fake/generate_dataset.py
+++ b/datasets/fake/generate_dataset.py
@@ -25,7 +25,7 @@ def randomImage(scale):
 
 imagesWithoutPrnu = [[randomImage(scale = 1) for _ in range(NUMBER_OF_IMAGES_PER_PHONE)] for phoneIndex in range(NUMBER_OF_PHONES)]
 
-prnus = [randomImage(scale = PRNU_FACTOR) for _ in range(NUMBER_OF_PHONES)]
+prnus = [np.array(Image.open('prnu.png').convert('F')) * PRNU_FACTOR / 255]
 
 imagesWithPrnu = [[imageWithoutPrnu + prnus[phoneIndex] for imageWithoutPrnu in imagesWithoutPrnu[phoneIndex]] for phoneIndex in range(NUMBER_OF_PHONES)]
 
@@ -75,6 +75,23 @@ plt.show()
 
 ##
 
+def toFileName(title):
+    return title.lower().replace(' ', '_').replace(',', '_')
+
+for title, image in zip(['Actual PRNU', 'First image without PRNU'], [prnus[0], imagesWithoutPrnu[0][0]]):
+    plt.title(title)
+    plt.imshow(image)
+    plt.savefig(title.lower().replace(' ', '_') + '.svg')
+
+for numberOfImages in [10 ** power for power in range(NUMBER_OF_ROWS)]:
+    title = 'First image with PRNU' if numberOfImages == 1 else f'Mean of first {numberOfImages:,} images with PRNU'
+    image = np.array(imagesWithPrnu[0][:numberOfImages]).mean(axis = 0)
+    plt.title(f'{title}\ni.e. estimated PRNU\nRMS with actual PRNU = {round(rmsDiffNumpy(image, prnus[0]), 4)}')
+    plt.imshow(image)
+    plt.savefig(f'{toFileName(title)}.svg')
+
+##
+
 # Compute CAI of phone images.
 caiImages = [[contextAdaptiveInterpolator(image.load(), image) for image in imagesWithPrnuPil[phoneIndex]] for phoneIndex in tqdm(range(NUMBER_OF_PHONES))]
 #caiImages[0][0].show()