First PRNU classifier acting randomly it seems

datasets/fake/generate_dataset.py

@@ -10,35 +10,68 @@ from rmsdiff import rmsdiff
 sys.path.insert(0, '../../algorithms/context_adaptive_interpolator/')
 
 from context_adaptive_interpolator import contextAdaptiveInterpolator
+from tqdm import tqdm
 
 IMAGE_SIZE = 64
+NUMBER_OF_PHONES = 10
+NUMBER_OF_IMAGES_PER_PHONE = 100
+# Compared to images.
+PRNU_FACTOR = 0.1
 
-# Is such `np.maximum` probabilistically correct with our theoretical method?
+# Is such `np.maximum` probabilistically correct with our theoretical method? See #19.
 def randomImage(scale):
     return np.maximum(np.random.normal(loc = 0, scale = scale, size = (IMAGE_SIZE, IMAGE_SIZE)), 0)
 
-prnu = randomImage(scale = 1)
+prnus = [randomImage(scale = 0.1) for _ in range(NUMBER_OF_PHONES)]
 
-images = [randomImage(scale = 10) + prnu for _ in range(100)]
-allImages = [prnu] + images
+images = [[randomImage(scale = 10) + prnus[phoneIndex] for _ in range(NUMBER_OF_IMAGES_PER_PHONE)] for phoneIndex in range(NUMBER_OF_PHONES)]
+
+# How to simplify?
+allImages = np.max([np.max(prnus) + np.max(images)])
 
 def toPilImage(npArray):
     npArray = (255 * npArray / np.max(allImages)).round()
     return Image.fromarray(np.uint8(npArray))
 
-prnu = toPilImage(prnu)
-#prnu.show()
+prnus = [toPilImage(prnu) for prnu in prnus]
+#prnus[0].show()
 
-images = [toPilImage(image) for image in images]
-#images[0].show()
+images = [[toPilImage(image) for image in images[phoneIndex]] for phoneIndex in range(NUMBER_OF_PHONES)]
+#images[0][0].show()
 
-caiImages = [contextAdaptiveInterpolator(image.load(), image) for image in images]
-for image, caiImage in zip(images, caiImages):
-    initialRmsDiff = rmsdiff(image, prnu)
-    caiRmsDiff = rmsdiff(caiImage, prnu)
-    #print(f'{initialRmsDiff=} {caiRmsDiff=}')
+caiImages = [[contextAdaptiveInterpolator(image.load(), image) for image in images[phoneIndex]] for phoneIndex in tqdm(range(NUMBER_OF_PHONES))]
 
-print('RMS diff with average image =', rmsdiff(toPilImage(np.array(images).mean(axis = 0)), prnu))
-# Should be better than above one. Maybe not the case here as the images are Gaussian noise.
-print('RMS diff with average CAI images =', rmsdiff(toPilImage(np.array(caiImages).mean(axis = 0)), prnu))
+def getPhoneIndexByNearestPrnu(estimatedPrnu):
+    nearestPrnuRmsDiff = None
+    nearestPrnuPhoneIndex = None
+    for phoneIndex, prnu in enumerate(prnus):
+        prnuRmsDiff = rmsdiff(estimatedPrnu, prnu)
+        if nearestPrnuRmsDiff is None or prnuRmsDiff < nearestPrnuRmsDiff:
+            nearestPrnuRmsDiff = prnuRmsDiff
+            nearestPrnuPhoneIndex = phoneIndex
+    return phoneIndex
+
+# What about CAI on `phoneImagesMean`?
+correctGuessesByAverageImages = 0
+correctGuessesByAverageCAIImages = 0
+for phoneIndex in range(NUMBER_OF_PHONES):
+    phoneImages = images[phoneIndex]
+    phoneCaiImages = caiImages[phoneIndex]
+
+    phoneImagesMean = toPilImage(np.array(phoneImages).mean(axis = 0))
+    caiImagesMean = toPilImage(np.array(phoneCaiImages).mean(axis = 0))
+    phonePrnu = prnus[phoneIndex]
+    print('RMS diff with average image =', rmsdiff(phoneImagesMean, phonePrnu))
+    print('RMS diff with average CAI images =', rmsdiff(caiImagesMean, phonePrnu))
+    guessedPhoneIndexByAverageImages = getPhoneIndexByNearestPrnu(phoneImagesMean)
+    guessedPhoneIndexByAverageCAIImages = getPhoneIndexByNearestPrnu(caiImagesMean)
+    #print(f'Actual phone index {phoneIndex}, guessed phone index {guessedPhoneIndex}')
+    correctGuessesByAverageImages += 1 if phoneIndex == guessedPhoneIndexByAverageImages else 0
+    correctGuessesByAverageCAIImages += 1 if phoneIndex == guessedPhoneIndexByAverageCAIImages else 0
+    #correct = phoneIndex == guessedPhoneIndex
+    #if correct:
+    #    correctGuesses += 1
+
+print(correctGuessesByAverageImages / NUMBER_OF_PHONES)
+print(correctGuessesByAverageCAIImages / NUMBER_OF_PHONES)