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base: Benjamin_Loison:prnu_written_as_such
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Benjamin_Loison:master Benjamin_Loison:prnu_written_as_such
Benjamin_Loison:evaluation
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compare: Benjamin_Loison:5fa61f7ff8ab20db9558d98d9f9c0290ab8616d6
Branches Tags
Benjamin_Loison:master Benjamin_Loison:prnu_written_as_such
Benjamin_Loison:evaluation

29 Commits
prnu_writt ... 5fa61f7ff8

Author SHA1 Message Date
Benjamin Loison
5fa61f7ff8 Make PRNU compatible with 4x4 split 2024-03-29 11:23:48 +01:00
Benjamin Loison
8351e46437 Comparison of an image without and with Gaussian noise and PRNU 2024-03-29 01:55:46 +01:00
Benjamin Loison
2f66e82f22 Show both RMS and RMS normalized 2024-03-29 01:47:01 +01:00
Benjamin Loison
3ccec5bbd0 Add RMS computation 2024-03-29 01:35:48 +01:00
Benjamin Loison
f297060f42 Split images in 4x4 to increase PRNU estimation accuracy 2024-03-29 01:21:26 +01:00
Benjamin Loison
9b57d3441c Render PRNU estimate taking into account all images 2024-03-29 01:10:31 +01:00
Benjamin Loison
d59a251b1f Revert to some extent previous commit 2024-03-29 01:07:48 +01:00
Benjamin Loison
4382b3d649 Add PRNU_FACTOR 0.1 and 0.01 view on a single figure 2024-03-29 01:05:28 +01:00
Benjamin Loison
dfe2540c02 Apply Context-Adaptive Interpolator 2024-03-29 00:06:13 +01:00
Benjamin Loison
9a3cfd7ba1 Add PRNU showing such 2024-03-28 22:24:16 +01:00
Benjamin Loison
ba5a1b742b #24: Make executions reproducible 2024-03-28 22:20:40 +01:00
Benjamin Loison
a99e942d3a Add datasets/noise_free_test_images/estimate_prnu.py 2024-03-28 17:06:58 +01:00
Benjamin Loison
0953fb7475 Add issue reference to datasets/fake/generate_dataset.py 2024-03-28 17:06:36 +01:00
Benjamin Loison
82e7026264 Add algorithms/image_utils/image_utils.py and move there randomGaussianImage and showImageWithMatplotlib 2024-03-28 16:32:38 +01:00
Benjamin Loison
70ccb094d5 Rename randomImage to randomGaussianImage 2024-03-28 16:18:32 +01:00
Benjamin Loison
64eaeddf98 Update annotations 2024-03-28 15:26:57 +01:00
Benjamin Loison
18d34a3101 Add datasets/noise_free_test_images/no_noise_images.zip.txt 2024-03-28 15:25:21 +01:00
Benjamin Loison
cbc778c4dc Move datasets/noise_free_test_images/noise_free_test_images.{pdf.txt,xopp.xml} to datasets/noise_free_test_images/webpage/ 2024-03-28 15:22:28 +01:00
Benjamin Loison
5af7afaf75 Add datasets/noise_free_test_images/ 2024-03-28 15:03:57 +01:00
Benjamin Loison
77d6c038fe Update annotations 2024-03-28 14:50:45 +01:00
Benjamin Loison
69368a9057 Add articles/Law of large numbers - Wikipedia/ 2024-03-28 14:50:41 +01:00
Benjamin Loison
abfc9772e8 Update annotations 2024-03-28 14:47:13 +01:00
Benjamin Loison
d716574973 Update annotations 2024-03-26 18:23:30 +01:00
Benjamin Loison
2093f2e613 Update annotations 2024-03-26 18:21:12 +01:00
Benjamin Loison
9028d4d12c Add __pycache__ to .gitignore 
Note that I do not precisely know if Pyzo or Python script shell execution generate `__pycache__`.
 2024-03-26 18:21:01 +01:00
Benjamin Loison
811b355989 Add articles/Shot noise - Wikipedia/ 2024-03-26 18:20:28 +01:00
Benjamin Loison
cfe718da7d Make x-axis logarithmic 2024-03-26 02:32:03 +01:00
Benjamin Loison
2bc13c5949 #21: Make a RMS curve depending on the number of images considered for the mean 2024-03-26 01:59:22 +01:00
Benjamin Loison
c2862eaf43 #21: Incorrect mean, as it is a RMS mean, not a mean of images 2024-03-26 01:55:08 +01:00
14 changed files with 340 additions and 13 deletions
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1
.gitignore vendored
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@@ -1,2 +1,3 @@
*.pdf
*.xopp
__pycache__

1
algorithms/context_adaptive_interpolator/context_adaptive_interpolator.py
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@@ -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()

10
algorithms/distance/rms_diff.py
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@@ -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)))

15
algorithms/image_utils/image_utils.py Normal file
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@@ -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)

1
articles/Law of large numbers - Wikipedia/Law of large numbers - Wikipedia.pdf.txt Normal file
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@@ -0,0 +1 @@
https://en.wikipedia.org/w/index.php?title=Law_of_large_numbers&oldid=1214684685

62
articles/Law of large numbers - Wikipedia/Law of large numbers - Wikipedia.xopp.xml Normal file
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articles/Shot noise - Wikipedia/Shot noise - Wikipedia.pdf.txt Normal file
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111
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<text font="Sans" size="11.00000000" x="327.01944284" y="309.08399263" color="#00c0ffff" ts="0" fn="">so pixel or black?</text>
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6
articles/Wu_ICIP_2012/Wu_ICIP_2012.xopp.xml
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38
datasets/fake/generate_dataset.py
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@@ -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 = [randomImage(scale = PRNU_FACTOR) for _ in range(NUMBER_OF_PHONES)]
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

66
datasets/noise_free_test_images/estimate_prnu.py Normal file
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@@ -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})')

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

1
datasets/noise_free_test_images/webpage/noise_free_test_images.pdf.txt Normal file
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@@ -0,0 +1 @@
https://web.archive.org/web/20221230200626/https://mcolom.perso.math.cnrs.fr/pages/no_noise_images/

39
datasets/noise_free_test_images/webpage/noise_free_test_images.xopp.xml Normal file
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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>
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