WIP: Getting PRNU by averaging on fake dataset

algorithms/context_adaptive_interpolator/context_adaptive_interpolator.py

@@ -19,11 +19,11 @@ def contextAdaptiveInterpolator(I, IImage, showProgress = False):
     # Accelerate computation. See #15.
     for m in range(1, IImage.size[0] - 1):
         for n in range(1, IImage.size[1] - 1):
-                e = I[m, n + 1]
+                e  = I[m    , n + 1]
                 se = I[m + 1, n + 1]
-                s = I[m + 1, n]
+                s  = I[m + 1, n]
                 sw = I[m + 1, n - 1]
-                w = I[m, n - 1]
+                w  = I[m    , n - 1]
                 nw = I[m - 1, n - 1]
                 no = I[m - 1, n]
                 ne = I[m - 1, n + 1]

algorithms/context_adaptive_interpolator/wiener_filter.py

@@ -5,7 +5,6 @@ def wienerFilter(r, rImage, Q, sigma_0, showProgress):
     h_wImage = Image.new('L', (rImage.size[0], rImage.size[1]))
     h_wImagePixels = h_wImage.load()
 
-    # Wiener filter.
     def h_w(hImage, h, i, j):
         # Equation (7)
         return h[i, j] * sigma(hImage, h, i, j) / (sigma(hImage, h, i, j) + sigma_0 ** 2)

datasets/fake/generate_dataset.py

@@ -13,41 +13,45 @@ from context_adaptive_interpolator import contextAdaptiveInterpolator
 from tqdm import tqdm
 
 IMAGE_SIZE = 64
-NUMBER_OF_PHONES = 10
+NUMBER_OF_PHONES = 1#0
 NUMBER_OF_IMAGES_PER_PHONE = 100
-# Compared to images.
+# Compared to images being 1.
 PRNU_FACTOR = 0.1
 
 # Generate PRNUs and images of phones.
 # 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)
+    return np.random.normal(loc = 0, scale = scale, size = (IMAGE_SIZE, IMAGE_SIZE))
 
 prnus = [randomImage(scale = PRNU_FACTOR) for _ in range(NUMBER_OF_PHONES)]
 
 images = [[randomImage(scale = 1) + 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))
+    return Image.fromarray(npArray)
 
-prnus = [toPilImage(prnu) for prnu in prnus]
-#prnus[0].show()
+def showImage(npArray):
+    npArray -= npArray.min()
+    npArray = (npArray / npArray.max()) * 255
+    Image.fromarray(npArray).show()
 
-images = [[toPilImage(image) for image in images[phoneIndex]] for phoneIndex in range(NUMBER_OF_PHONES)]
-#images[0][0].show()
+prnusPil = [toPilImage(prnu) for prnu in prnus]
+#showImage(prnus[0])
+
+imagesPil = [[toPilImage(image) for image in images[phoneIndex]] for phoneIndex in range(NUMBER_OF_PHONES)]
+#showImage(images[0][0])
 
 # Compute CAI of phone images.
-caiImages = [[contextAdaptiveInterpolator(image.load(), image) for image in images[phoneIndex]] for phoneIndex in tqdm(range(NUMBER_OF_PHONES))]
+caiImages = [[contextAdaptiveInterpolator(image.load(), image) for image in imagesPil[phoneIndex]] for phoneIndex in tqdm(range(NUMBER_OF_PHONES))]
+#caiImages[0][0].show()
 
 # Guess the phone by consider the one reaching the lowest RMS difference between the estimated PRNU and the actual one.
 def getPhoneIndexByNearestPrnu(estimatedPrnu):
     nearestPrnuRmsDiff = None
     nearestPrnuPhoneIndex = None
-    for phoneIndex, prnu in enumerate(prnus):
+    for phoneIndex, prnu in enumerate(prnusPil):
         prnuRmsDiff = rmsdiff(estimatedPrnu, prnu)
         if nearestPrnuRmsDiff is None or prnuRmsDiff < nearestPrnuRmsDiff:
             nearestPrnuRmsDiff = prnuRmsDiff
@@ -58,15 +62,16 @@ def getPhoneIndexByNearestPrnu(estimatedPrnu):
 # What about CAI on `phoneImagesMean`? See https://gitea.lemnoslife.com/Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones/issues/9#issuecomment-1369.
 correctGuessesByMeanImages = 0
 correctGuessesByMeanCAIImages = 0
+# Maybe make sense only because Gaussian images here.
 correctGuessesByCAIImagesMean = 0
 for phoneIndex in range(NUMBER_OF_PHONES):
-    phoneImages = images[phoneIndex]
+    phoneImages = imagesPil[phoneIndex]
     phoneCaiImages = caiImages[phoneIndex]
 
     phoneImagesMean = toPilImage(np.array(phoneImages).mean(axis = 0))
     caiImagesMean = toPilImage(np.array(phoneCaiImages).mean(axis = 0))
     caiOverPhoneImagesMean = contextAdaptiveInterpolator(phoneImagesMean.load(), phoneImagesMean)
-    phonePrnu = prnus[phoneIndex]
+    phonePrnu = prnusPil[phoneIndex]
     print('RMS diff with mean image =', rmsdiff(phoneImagesMean, phonePrnu))
     print('RMS diff with mean CAI images =', rmsdiff(caiImagesMean, phonePrnu))
     print('RMS diff with CAI images mean =', rmsdiff(caiOverPhoneImagesMean, phonePrnu))