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base: Benjamin_Loison:a6ef3977dd364f6dbde12f491aa81a6b4f5be305
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Benjamin_Loison:master Benjamin_Loison:prnu_written_as_such
Benjamin_Loison:evaluation
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compare: Benjamin_Loison:prnu_written_as_such
Branches Tags
Benjamin_Loison:master Benjamin_Loison:prnu_written_as_such
Benjamin_Loison:evaluation

1 Commits
a6ef3977dd ... prnu_writt

Author SHA1 Message Date
Benjamin Loison
ccb2b4bd86 #21: Write PRNU as such to make figures clearer 2024-03-26 03:15:34 +01:00
66 changed files with 36 additions and 2089 deletions
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1
.gitignore vendored
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@@ -1,3 +1,2 @@
*.pdf
*.xopp
__pycache__

1
algorithms/context_adaptive_interpolator/context_adaptive_interpolator.py
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@@ -6,7 +6,6 @@ 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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@@ -14,13 +14,5 @@ def rmsDiffPil(im1, im2):
map(lambda h, i: h*(i**2), h, range(256))
) / (float(im1.size[0]) * im1.size[1]))
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)
def rmsDiffNumpy(image0, image1):
return np.sqrt(np.mean(np.square(image0 - image1)))

41
algorithms/image_utils/image_utils.py
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@@ -1,41 +0,0 @@
from PIL import Image, ImageFont, ImageDraw
import numpy as np
from matplotlib import pyplot as plt
import os
def randomGaussianImage(scale, size):
return np.random.normal(loc = 0, scale = scale, size = size)
# `cmap` source: https://matplotlib.org/3.8.0/api/_as_gen/matplotlib.pyplot.imshow.html
def showImageWithMatplotlib(npArray, title = None, cmap = 'viridis'):
if title is not None:
plt.title(title)
plt.imshow(npArray, cmap = cmap)
plt.show()
def toPilImage(npArray):
return Image.fromarray(npArray)
def getPrnuShownAsSuch(size, gaussianNoise = 0):
# Supports `WIDTH` > `HEIGHT` and conversely.
WIDTH, HEIGHT = size
TEXT = 'PRNU'
imagePil = Image.new('L', size)
draw = ImageDraw.Draw(imagePil)
fontPath = os.path.expanduser('~/.local/share/fonts/impact.ttf')
for fontSize in range(1, HEIGHT + 1):
font = ImageFont.truetype(fontPath, fontSize)
if font.getlength(TEXT) > WIDTH:
break
# Center vertically, especially in the case `HEIGHT` > `WIDTH`.
draw.text((0, HEIGHT // 2 - fontSize // 2), TEXT, 255, font = font)
imageNpArray = np.array(imagePil)
gaussianNoiseNpArray = randomGaussianImage(gaussianNoise, size[::-1])
#prnuShownAsSuch = imageNpArray + gaussianNoiseNpArray
prnuShownAsSuch = imageNpArray
for y in range(HEIGHT):
for x in range(WIDTH):
if prnuShownAsSuch[y, x] != 0:
prnuShownAsSuch[y, x] += gaussianNoiseNpArray[y, x]
return prnuShownAsSuch

1
articles/Additive white Gaussian noise - Wikipedia/Additive white Gaussian noise - Wikipedia.pdf.txt
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https://en.wikipedia.org/w/index.php?title=Additive_white_Gaussian_noise&oldid=1181981955

71
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articles/Bayer filter - Wikipedia/Bayer filter - Wikipedia.pdf.txt
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articles/Bernoulli distribution - Wikipedia/Bernoulli distribution - Wikipedia.pdf.txt
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articles/XnView - Wikipedia/XnView - Wikipedia.pdf.txt
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55
datasets/fake/generate_dataset.py
View File

@@ -1,5 +1,3 @@
# 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
@@ -12,10 +10,6 @@ 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
@@ -24,38 +18,30 @@ 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.
imagesWithoutPrnu = [[randomGaussianImage(scale = 1, size = IMAGE_SIZE_SHAPE) for _ in range(NUMBER_OF_IMAGES_PER_PHONE)] for phoneIndex in range(NUMBER_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))
prnus = [randomGaussianImage(scale = PRNU_FACTOR, size = IMAGE_SIZE_SHAPE) for _ 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]
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()
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()
##
def showImageWithMatplotlib(npArray):
plt.imshow(npArray)
plt.show()
NUMBER_OF_ROWS = 5
NUMBER_OF_COLUMNS = 3
@@ -89,6 +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()

100
datasets/noise_free_test_images/estimate_prnu.py
View File

@@ -1,100 +0,0 @@
# 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, getPrnuShownAsSuch
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
np.random.seed(0)
SPLIT_N_X_N_S = [1, 2, 4]
# len(SPLIT_N_X_N_S)
fig, axes = plt.subplots(2, 4)
fig.suptitle('PRNU estimation with different number of images having Gaussian noise and Gaussian noised PRNU')
for splitNXNIndex, splitNXN in enumerate(SPLIT_N_X_N_S):
IMAGE_SIZE_SHAPE = [dimension // splitNXN for dimension in (704, 469)]
#prnuNpArray = 255 * randomGaussianImage(scale = PRNU_FACTOR, size = IMAGE_SIZE_SHAPE)
prnuNpArray = getPrnuShownAsSuch(IMAGE_SIZE_SHAPE, 255) * PRNU_FACTOR
def isIn256Range(x):
return 0 <= x and x <= 255
imagesPrnuEstimateNpArray = []
isFirstImage = True
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 = IMAGE_SIZE_SHAPE[1]
n = IMAGE_SIZE_SHAPE[0]
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:
#print(imageWithoutPrnuNpArrayTile.shape, tuple(IMAGE_SIZE_SHAPE[::-1]))
#if imageWithoutPrnuNpArrayTile.shape != tuple(IMAGE_SIZE_SHAPE[::-1]):
# continue
imageNoise = randomGaussianImage(scale = 255 * NOISE_FACTOR, size = imageWithoutPrnuNpArrayTile.shape)
imageWithPrnuNpArray = imageWithoutPrnuNpArrayTile + prnuNpArray + imageNoise
if splitNXNIndex == 0 and isFirstImage:
axis = axes[0]
axis[0].set_title('First image without noise')
axis[0].imshow(imageWithoutPrnuNpArrayTile)
axis[1].set_title('Actual Gaussian noised PRNU')
axis[1].imshow(prnuNpArray)
axis[2].set_title('F. i. with G. n.')
axis[2].imshow(imageWithoutPrnuNpArray + imageNoise)
axis[3].set_title('F. i. with G. n. and PRNU')
axis[3].imshow(imageWithoutPrnuNpArray + prnuNpArray + imageNoise)
isFirstImage = False
#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)
title = f'RMS with actual PRNU: {rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray):.4f}\n(normalized RMS: {rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray, True):.4f})'
axis = axes[1]
axis[splitNXNIndex].set_title(f'Number of images: {len(imagesPrnuEstimateNpArray)}\n{title}')
axis[splitNXNIndex].imshow(cameraPrnuEstimateNpArray)
axes[1][3].axis('off')
plt.tight_layout()
plt.show()

1
datasets/noise_free_test_images/no_noise_images.zip.txt
View File

@@ -1 +0,0 @@
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
View File

@@ -1 +0,0 @@
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
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92
datasets/raise/extract_noise.py
View File

@@ -1,92 +0,0 @@
#!/usr/bin/env python
import skimage.restoration
from skimage import img_as_float
import numpy as np
from PIL import Image
import os
from tqdm import tqdm
import csv
import rawpy
imagesFolderPath = '/mnt/HDD0/raise'
imagesFolderPathFileName = imagesFolderPath.replace('/', '_')
denoiser = 'wavelet'
npArrayFilePath = f'mean_{imagesFolderPathFileName}_{denoiser}.npy'
denoise = getattr(skimage.restoration, f'denoise_{denoiser}')
mean = None
numberOfImagesInMean = 0
imagesFileNames = os.listdir(imagesFolderPath + '/png')
requiresRaiseFiltering = True
if requiresRaiseFiltering:
files = {}
with open('RAISE_all.csv') as csvfile:
reader = csv.DictReader(csvfile)
for row in tqdm(list(reader), 'CSV parsing'):
file = row['File'] + '.png'
files[file] = row
imagesFileNames = [imageFileName for imageFileName in tqdm(imagesFileNames, 'Filtering images') if files[imageFileName]['Device'] == 'Nikon D7000' and Image.open(f'{imagesFolderPath}/png/{imageFileName}').size == (4946, 3278)]
#imagesFileNames = [f'DSC0{imageIndex}.ARW' for imageIndex in range(2807, 2911)]
minColor = None
maxColor = None
def treatImage(imageFileName, computeExtremes = False):
global mean, numberOfImagesInMean, minColor, maxColor
imageFileName = imageFileName.replace('.png', '.NEF')
imageFilePath = f'{imagesFolderPath}/nef/{imageFileName}'
if imageFileName.endswith('.NEF') or imageFileName.endswith('.ARW'):
with rawpy.imread(imageFilePath) as raw:
colorDesc = raw.color_desc.decode('ascii')
assert colorDesc == 'RGBG'
assert np.array_equal(raw.raw_pattern, np.array([[0, 1], [3, 2]], dtype = np.uint8))
rawImageVisible = raw.raw_image_visible.copy()
greenRawImageVisible = rawImageVisible[1::2, ::2]
if computeExtremes:
greenRawImageVisibleMin = greenRawImageVisible.min()
greenRawImageVisibleMax = greenRawImageVisible.max()
if minColor is None or greenRawImageVisibleMin < minColor:
minColor = greenRawImageVisibleMin
if maxColor is None or greenRawImageVisibleMax > maxColor:
maxColor = greenRawImageVisibleMax
return
imageNpArray = (greenRawImageVisible - minColor) / (maxColor - minColor)
# Pay attention to range of values expected by the denoiser.
# Indeed if provide the thousands valued raw image, then the denoiser only returns values between 0 and 1 and making the difference between both look pointless.
else:
imagePil = Image.open(imageFilePath)
imageNpArray = img_as_float(np.array(imagePil))
# Could make not `.nef` compatible.
match denoiser:
case 'wavelet':
imageDenoisedNpArray = denoise(imageNpArray, rescale_sigma=True)
case 'bilateral':
imageDenoisedNpArray = denoise(imageNpArray, sigma_color=0.05, sigma_spatial=15)
case 'tv_chambolle':
imageDenoisedNpArray = denoise(imageNpArray, weight=0.2)
imageNoiseNpArray = imageNpArray - imageDenoisedNpArray
if mean is None:
mean = imageNoiseNpArray
else:
mean = ((mean * numberOfImagesInMean) + imageNoiseNpArray) / (numberOfImagesInMean + 1)
numberOfImagesInMean += 1
for imageFileName in tqdm(imagesFileNames, 'Computing extremes of images'):
treatImage(imageFileName, computeExtremes = True)
# To skip this step next time.
# Maybe thanks to `rawpy.RawPy` fields, possibly stating device maximal value, can avoid doing so to some extent.
print(f'{minColor=}')
print(f'{maxColor=}')
for imageFileName in tqdm(imagesFileNames, 'Denoising images'):
treatImage(imageFileName)
with open(npArrayFilePath, 'wb') as f:
np.save(f, mean)

7
datasets/raise/show_mean_noise.py
View File

@@ -1,7 +0,0 @@
import numpy as np
import matplotlib.pyplot as plt
npArray = np.load('mean_rafael_arw_png_sky_wavelet.npy')
npArrayMin = npArray.min()
print(f'{npArrayMin=}')
plt.imsave('np_array.png', npArray - npArrayMin)

53
datasets/raise/split_and_compare_prnus_of_subgroups.py
View File

@@ -1,53 +0,0 @@
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
from skimage.restoration import denoise_tv_chambolle
from skimage import img_as_float
import sys
sys.path.insert(0, '../../algorithms/image_utils/')
from image_utils import showImageWithMatplotlib, toPilImage
sys.path.insert(0, '../../algorithms/distance/')
from rms_diff import rmsDiffNumpy
NUMBER_OF_SUBGROUPS = 1
IMAGES_FOLDER = 'flat-field/TIF'
imagesFileNames = os.listdir(IMAGES_FOLDER)
numberOfImagesPerSubgroup = len(imagesFileNames) // NUMBER_OF_SUBGROUPS
# 1
numberOfImagesThresholds = range(numberOfImagesPerSubgroup, numberOfImagesPerSubgroup + 1)
# Assume random image order to not introduce a bias.
subgroupsPrnuEstimatesNpArray = []
for subgroupIndex in range(NUMBER_OF_SUBGROUPS):
imagesPrnuEstimateNpArray = []
for imageFileName in tqdm(imagesFileNames[numberOfImagesPerSubgroup * subgroupIndex : numberOfImagesPerSubgroup * (subgroupIndex + 1)]):
imagePath = f'{IMAGES_FOLDER}/{imageFileName}'
imagePil = Image.open(imagePath)
imageNpArray = img_as_float(np.array(imagePil))
imagePrnuEstimateNpArray = imageNpArray - denoise_tv_chambolle(imageNpArray, weight=0.2, channel_axis=-1)
imagesPrnuEstimateNpArray += [imagePrnuEstimateNpArray]
subgroupPrnuEstimateNpArray = []
# Not efficient mean computation.
for numberOfImagesIndex, numberOfImages in enumerate(numberOfImagesThresholds):
subgroupPrnuEstimateNpArray += [np.array(imagesPrnuEstimateNpArray[:numberOfImages]).mean(axis = 0)]
plt.imsave('prnu.png', subgroupPrnuEstimateNpArray[-1])
subgroupsPrnuEstimatesNpArray += [subgroupPrnuEstimateNpArray]
rmss = []
for numberOfImagesIndex, numberOfImages in enumerate(numberOfImagesThresholds):
rms = rmsDiffNumpy(subgroupsPrnuEstimatesNpArray[0][numberOfImagesIndex], subgroupsPrnuEstimatesNpArray[1][numberOfImagesIndex])
rmss += [rms]
plt.title('RMS between both subgroups estimated PRNUs for a given number of images among them')
plt.xlabel('Number of images of each subgroup')
plt.ylabel('RMS between both subgroups estimated PRNUs')
plt.plot(rmss)
plt.show()

1
datasets/raise/website/RAISE - Dataset Download/RAISE - Dataset Download.pdf.txt
View File

@@ -1 +0,0 @@
https://web.archive.org/web/20221206120232/http://loki.disi.unitn.it/RAISE/download.html

36
datasets/raise/website/RAISE - Dataset Download/RAISE - Dataset Download.xopp.xml
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