Make PRNU compatible with 4x4 split

Note that I do not precisely know if Pyzo or Python script shell execution generate `__pycache__`.

.gitignore

@@ -1,2 +1,3 @@
 *.pdf
 *.xopp
+__pycache__

algorithms/context_adaptive_interpolator/context_adaptive_interpolator.py

@@ -6,6 +6,7 @@ from wiener_filter import wienerFilter
 
 # Assume greyscale PIL image passed.
 # What about other color channels? See #11.
+# `THRESHOLD` seems to have been designed to assume 256 range based images.
 def contextAdaptiveInterpolator(I, IImage, showProgress = False):
     rImage = Image.new('L', (IImage.size[0] - 2, IImage.size[1] - 2))
     r = rImage.load()

algorithms/distance/rms_diff.py

@@ -14,5 +14,13 @@ def rmsDiffPil(im1, im2):
         map(lambda h, i: h*(i**2), h, range(256))
     ) / (float(im1.size[0]) * im1.size[1]))
 
-def rmsDiffNumpy(image0, image1):
+def normalizeImage(image):
+    image = image - image.min()
+    image = image / image.max()
+    return image
+
+def rmsDiffNumpy(image0, image1, normalize = False):
+    if normalize:
+        image0 = normalizeImage(image0)
+        image1 = normalizeImage(image1)
     return np.sqrt(np.mean(np.square(image0 - image1)))

algorithms/image_utils/image_utils.py

@@ -0,0 +1,15 @@
+from PIL import Image
+import numpy as np
+from matplotlib import pyplot as plt
+
+def randomGaussianImage(scale, size):
+    return np.random.normal(loc = 0, scale = scale, size = size)
+
+def showImageWithMatplotlib(npArray, title = None):
+    if title is not None:
+        plt.title(title)
+    plt.imshow(npArray)
+    plt.show()
+
+def toPilImage(npArray):
+    return Image.fromarray(npArray)

articles/Law of large numbers - Wikipedia/Law of large numbers - Wikipedia.pdf.txt

@@ -0,0 +1 @@
+https://en.wikipedia.org/w/index.php?title=Law_of_large_numbers&oldid=1214684685

articles/Law of large numbers - Wikipedia/Law of large numbers - Wikipedia.xopp.xml

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articles/Shot noise - Wikipedia/Shot noise - Wikipedia.pdf.txt

@@ -0,0 +1 @@
+https://en.wikipedia.org/w/index.php?title=Shot_noise&oldid=1210973018

articles/Shot noise - Wikipedia/Shot noise - Wikipedia.xopp.xml

@@ -0,0 +1,111 @@
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+  <title>Xournal++ document - see https://github.com/xournalpp/xournalpp</title>
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+      <text font="Sans" size="11.00000000" x="173.37040718" y="82.43216394" color="#00c0ffff" ts="0" fn="">example of generating Poisson noise?</text>
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datasets/fake/generate_dataset.py

@@ -1,3 +1,5 @@
+# Notes: https://gitea.lemnoslife.com/Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones/issues/21
+
 import numpy as np
 from matplotlib import pyplot as plt
 from PIL import Image
@@ -10,6 +12,10 @@ from rms_diff import rmsDiffPil, rmsDiffNumpy
 sys.path.insert(0, '../../algorithms/context_adaptive_interpolator/')
 
 from context_adaptive_interpolator import contextAdaptiveInterpolator
+
+sys.path.insert(0, '../../algorithms/image_utils/')
+
+from image_utils import showImageWithMatplotlib, randomGaussianImage, toPilImage
 from tqdm import tqdm
 
 IMAGE_SIZE = 64
@@ -18,30 +24,38 @@ NUMBER_OF_IMAGES_PER_PHONE = 10_000
 # Compared to images being 1.
 PRNU_FACTOR = 0.1
 
+IMAGE_SIZE_SHAPE = (IMAGE_SIZE, IMAGE_SIZE)
+
+np.random.seed(0)
+
 # Generate PRNUs and images of phones.
-# Is such `np.maximum` probabilistically correct with our theoretical method? See #19.
-def randomImage(scale):
-    return np.random.normal(loc = 0, scale = scale, size = (IMAGE_SIZE, IMAGE_SIZE))
+imagesWithoutPrnu = [[randomGaussianImage(scale = 1, size = IMAGE_SIZE_SHAPE) for _ in range(NUMBER_OF_IMAGES_PER_PHONE)] for phoneIndex in range(NUMBER_OF_PHONES)]
 
-imagesWithoutPrnu = [[randomImage(scale = 1) for _ in range(NUMBER_OF_IMAGES_PER_PHONE)] for phoneIndex in range(NUMBER_OF_PHONES)]
-
-prnus = [np.array(Image.open('prnu.png').convert('F')) * PRNU_FACTOR / 255]
+prnus = [randomGaussianImage(scale = PRNU_FACTOR, size = IMAGE_SIZE_SHAPE) for _ in range(NUMBER_OF_PHONES)]
 
 imagesWithPrnu = [[imageWithoutPrnu + prnus[phoneIndex] for imageWithoutPrnu in imagesWithoutPrnu[phoneIndex]] for phoneIndex in range(NUMBER_OF_PHONES)]
 
 allImages = np.max([np.max(imagesWithoutPrnu) + np.max(prnus) + np.max(imagesWithPrnu)])
 
-def toPilImage(npArray):
-    return Image.fromarray(npArray)
-
 def showImageWithPil(npArray):
     npArray -= npArray.min()
     npArray = (npArray / npArray.max()) * 255
     Image.fromarray(npArray).show()
 
-def showImageWithMatplotlib(npArray):
-    plt.imshow(npArray)
-    plt.show()
+plt.title('RMS between actual PRNU and the mean of the first $N$ images with PRNU (i.e. estimated PRNU)')
+plt.xlabel('$N$ first images with PRNU')
+plt.ylabel('RMS')
+plt.xscale('log')
+rmss = []
+mean = np.zeros(IMAGE_SIZE_SHAPE)
+for imageIndex in range(NUMBER_OF_IMAGES_PER_PHONE):
+    mean = (mean * imageIndex + imagesWithPrnu[0][imageIndex]) / (imageIndex + 1)
+    rms = rmsDiffNumpy(mean, prnus[0])
+    rmss += [rms]
+plt.plot(rmss)
+plt.show()
+
+##
 
 NUMBER_OF_ROWS = 5
 NUMBER_OF_COLUMNS = 3
@@ -75,23 +89,6 @@ 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()

datasets/noise_free_test_images/estimate_prnu.py

@@ -0,0 +1,66 @@
+# Notes: https://gitea.lemnoslife.com/Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones/issues/25
+
+import os
+from PIL import Image
+import numpy as np
+import matplotlib.pyplot as plt
+import sys
+
+sys.path.insert(0, '../../algorithms/image_utils/')
+
+from image_utils import showImageWithMatplotlib, randomGaussianImage, toPilImage
+
+sys.path.insert(0, '../../algorithms/context_adaptive_interpolator/')
+
+from context_adaptive_interpolator import contextAdaptiveInterpolator
+
+sys.path.insert(0, '../../algorithms/distance/')
+
+from rms_diff import rmsDiffNumpy
+
+from skimage.restoration import denoise_tv_chambolle
+
+datasetPath = 'no_noise_images'
+# Note that contrarily to `datasets/fake/`, here we do not have images being Gaussian with `scale` `1` but actual images with pixel values between 0 and 255.
+# In addition to the range difference, note that the distribution in the first set of images was a Gaussian and here is very different and specific.
+PRNU_FACTOR = 0.01
+NOISE_FACTOR = 0.1
+IMAGE_SIZE_SHAPE = (469, 704)
+IMAGE_SIZE_SHAPE_4x4 = [size // 4 for size in IMAGE_SIZE_SHAPE]
+
+np.random.seed(0)
+
+#prnuNpArray = 255 * randomGaussianImage(scale = PRNU_FACTOR, size = IMAGE_SIZE_SHAPE)
+prnuPil = Image.open('prnu_4x4_noise.png').convert('F')
+prnuNpArray = np.array(prnuPil) * PRNU_FACTOR
+
+def isIn256Range(x):
+    return 0 <= x and x <= 255
+
+imagesPrnuEstimateNpArray = []
+
+for imageName in os.listdir(datasetPath):
+    if imageName.endswith('.png'):
+        imagePath = f'{datasetPath}/{imageName}'
+        imageWithoutPrnuPil = Image.open(imagePath).convert('F')
+        imageWithoutPrnuNpArray = np.array(imageWithoutPrnuPil)
+
+        m = imageWithoutPrnuNpArray.shape[0] // 4
+        n = imageWithoutPrnuNpArray.shape[1] // 4
+
+        imageWithoutPrnuNpArrayTiles = [imageWithoutPrnuNpArray[x : x + m, y : y + n] for x in range(0, imageWithoutPrnuNpArray.shape[0], m) for y in range(0, imageWithoutPrnuNpArray.shape[1], n)]
+        for imageWithoutPrnuNpArrayTile in imageWithoutPrnuNpArrayTiles:
+            imageNoise = randomGaussianImage(scale = 255 * NOISE_FACTOR, size = imageWithoutPrnuNpArrayTile.shape)
+            imageWithPrnuNpArray = imageWithoutPrnuNpArrayTile + prnuNpArray + imageNoise
+            #assert all([isIn256Range(extreme) for extreme in [imageWithPrnuNpArray.max(), imageWithPrnuNpArray.min()]]), 'Adding the PRNU resulted in out of 256 bounds image'
+            imageWithPrnuPil = toPilImage(imageWithPrnuNpArray)
+            #imagePrnuEstimatePil = contextAdaptiveInterpolator(imageWithPrnuPil.load(), imageWithPrnuPil)
+            #imagePrnuEstimateNpArray = np.array(imagePrnuEstimatePil)
+            imagePrnuEstimateNpArray = imageWithPrnuNpArray - denoise_tv_chambolle(imageWithPrnuNpArray, weight=0.2, channel_axis=-1)
+
+            imagesPrnuEstimateNpArray += [imagePrnuEstimateNpArray]
+
+cameraPrnuEstimateNpArray = np.array(imagesPrnuEstimateNpArray).mean(axis = 0)
+rms = rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray, True)
+showImageWithMatplotlib(cameraPrnuEstimateNpArray, f'Camera PRNU estimate\nRMS with actual one: {rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray):.4f} (normalized RMS: {rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray, True):.4f})')
+

datasets/noise_free_test_images/no_noise_images.zip.txt

@@ -0,0 +1 @@
+https://web.archive.org/web/20220121204219/https://mcolom.perso.math.cnrs.fr/download/no_noise_images/no_noise_images.zip

datasets/noise_free_test_images/webpage/noise_free_test_images.pdf.txt

@@ -0,0 +1 @@
+https://web.archive.org/web/20221230200626/https://mcolom.perso.math.cnrs.fr/pages/no_noise_images/

datasets/noise_free_test_images/webpage/noise_free_test_images.xopp.xml

@@ -0,0 +1,39 @@
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+<xournal creator="Xournal++ 1.1.3" fileversion="4">
+  <title>Xournal++ document - see https://github.com/xournalpp/xournalpp</title>
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+    <background type="pdf" domain="absolute" filename="/home/benjamin/Desktop/bens_folder/school/ens/asp/aria/internship/work/datasets/noise_free_test_images/webpage/noise_free_test_images.pdf" pageno="1"/>
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+      <text font="Sans" size="11.00000000" x="45.82731792" y="353.90876455" color="#00c0ffff" ts="0" fn="">legends not matching actual webpage web-browser rendering</text>
+      <text font="Sans" size="11.00000000" x="48.63067627" y="32.63059998" color="#00c0ffff" ts="0" fn="">Almost identical content as `no_noise_images.zip/readme.txt`.</text>
+    </layer>
+  </page>
+  <page width="596.00000000" height="842.00000000">
+    <background type="pdf" pageno="2"/>
+    <layer/>
+  </page>
+  <page width="596.00000000" height="842.00000000">
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