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Author SHA1 Message Date
Benjamin Loison
ccb2b4bd86
#21: Write PRNU as such to make figures clearer 2024-03-26 03:15:34 +01:00
271 changed files with 44 additions and 12705 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, dtype = np.float64)
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/2007-dirik-sencar-memon--source-camera-identification-based-on-sensor-dust-characteristics/2007-dirik-sencar-memon--source-camera-identification-based-on-sensor-dust-characteristics.pdf.txt
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@ -1 +0,0 @@
https://ieeexplore.ieee.org/abstract/document/4218950

63
articles/2007-dirik-sencar-memon--source-camera-identification-based-on-sensor-dust-characteristics/2007-dirik-sencar-memon--source-camera-identification-based-on-sensor-dust-characteristics.xopp.xml
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articles/A comparison study of CNN denoisers on PRNU extraction/A comparison study of CNN denoisers on PRNU extraction.pdf.txt
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https://web.archive.org/web/20240521094929/https://arxiv.org/pdf/2112.02858

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articles/A context-adaptive SPN predictor for trustworthy source camera identification/A context-adaptive SPN predictor for trustworthy.pdf.txt
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<text font="Sans" size="12.00000000" x="4.21063267" y="76.87832085" color="#00c0ffff" ts="0" fn="">summary:
PRNU estimation using (6) (have to investigate the denoiser used)
pre-process PRNU estimation:
- remove for each row and column its mean
- if visually identifiable patterns in ZM(K), then Wiener filter in Fourier domain
- high-pass filtering in the Fourier domain</text>
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<text font="Sans" size="12.00000000" x="516.33007812" y="462.80007935" color="#00c0ffff" ts="0" fn="">to check</text>
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<text font="Sans" size="12.00000000" x="481.45550537" y="474.84460449" color="#00c0ffff" ts="0" fn="">dot equal meaning?
not considering O(x&#xB2;)
it seems correct</text>
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<teximage text="\mathbf{X} \text{ and } \mathbf{Y} \text{ seem to be images not sets of images}\\ \text{so } \mathbf{\bar{X}} \text{ seems to be the average pixel value of } \mathbf{X} \text{ and not the average image}" left="45.48101807" top="625.96652830" right="321.24827059" bottom="651.70501709"/>
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<teximage text="\text{It seems that: } \mathbf{\Xi} = \mathbf{I}^{(0)}-\mathbf{\hat{I}}^{(0)}+(\mathbf{I}^{(0)}-\mathbf{I})\mathbf{K}+\mathbf{\Theta}" left="109.97003174" top="134.90631104" right="321.23983951" bottom="155.90631104"/>
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<text font="Sans" size="12.00000000" x="210.09564209" y="213.57748413" color="#00c0ffff" ts="0" fn="">how?</text>
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<text font="Sans" size="12.00000000" x="263.93231201" y="435.04779053" color="#00c0ffff" ts="0" fn="">to check</text>
<text font="Sans" size="12.00000000" x="167.76638794" y="520.64541626" color="#00c0ffff" ts="0" fn="">why?</text>
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<text font="Sans" size="12.00000000" x="167.63052368" y="618.00769043" color="#00c0ffff" ts="0" fn="">why?</text>
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<teximage text="\text{why } E\left[\sum_{k=1}^N(\mathbf{I}_k)^2\right] = \sum_{k=1}^N(\mathbf{I}_k)^2?" left="183.49615479" top="591.16033936" right="300.89061988" bottom="625.16033936"/>
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<text font="Sans" size="12.00000000" x="2.33728027" y="715.83953857" color="#00c0ffff" ts="0" fn="">so there is no better *distance* to attribute a source camera or I am misunderstanding the purpose
of the ML?</text>
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<text font="Sans" size="12.00000000" x="317.21777344" y="8.99786377" color="#00c0ffff" ts="0" fn="">in theory we would not like any saturation,
is there a physical sensor limitation avoiding
saturation?</text>
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<text font="Sans" size="12.00000000" x="325.64184570" y="125.86141968" color="#00c0ffff" ts="0" fn="">just describing these terms?</text>
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<text font="Sans" size="12.00000000" x="375.60253906" y="70.74871826" color="#00c0ffff" ts="0" fn="">to check from (7) but makes sense</text>
<text font="Sans" size="12.00000000" x="319.67297363" y="138.03695679" color="#00c0ffff" ts="0" fn="">seems to make sense but what precise
argument is used here?</text>
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<text font="Sans" size="12.00000000" x="387.70733643" y="225.17886353" color="#00c0ffff" ts="0" fn="">argument?</text>
<text font="Sans" size="12.00000000" x="427.26794434" y="200.27055359" color="#00c0ffff" ts="0" fn="">argument?</text>
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<text font="Sans" size="12.00000000" x="291.08165394" y="565.81793919" color="#00c0ffff" ts="0" fn="">it seems that the gain is only introduced at the color
interpolation step</text>
<text font="Sans" size="12.00000000" x="474.48402594" y="649.18218944" color="#00c0ffff" ts="0" fn="">like?</text>
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<text font="Sans" size="12.00000000" x="318.42626146" y="675.00273397" color="#00c0ffff" ts="0" fn="">related to DSNU row/column artifacts?</text>
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<teximage text="= \sum_{k=1}^N (\mathbf{I}_k)^2(\mathbf{W}_k / \mathbf{I}_k - \mathbf{K})" left="3.44375610" top="-3.67115784" right="119.75775610" bottom="37.48584216"/>
<teximage text="= \sum_{k=1}^N \mathbf{I}_k(\mathbf{W}_k - \mathbf{K}\mathbf{I}_k)" left="3.23849487" top="30.28213501" right="104.01349487" bottom="71.43913501"/>
<teximage text="\implies \sum_{k=1}^N \frac{\mathbf{W}_k / \mathbf{I}_k - \mathbf{K}}{1 / (\mathbf{I}_k)^2} = 0" left="153.24240112" top="-1.43804932" right="276.02523272" bottom="39.56195068"/>
<teximage text="\iff \sum_{k=1}^N \frac{\mathbf{W}_k / \mathbf{I}_k}{1 / (\mathbf{I}_k)^2} - \sum_{k=1}^N \frac{\mathbf{K}}{1 / (\mathbf{I}_k)^2} = 0" left="149.72015381" top="31.34738159" right="316.63015381" bottom="72.50438159"/>
<teximage text="\iff \sum_{k=1}^N \frac{\mathbf{W}_k / \mathbf{I}_k}{1 / (\mathbf{I}_k)^2} = \sum_{k=1}^N \frac{\mathbf{K}}{1 / (\mathbf{I}_k)^2}" left="145.67276001" top="66.19903564" right="295.42476001" bottom="107.35603564"/>
<teximage text="\iff \sum_{k=1}^N \frac{\mathbf{W}_k / \mathbf{I}_k}{1 / (\mathbf{I}_k)^2} = \mathbf{K} \sum_{k=1}^N \frac{1}{1 / (\mathbf{I}_k)^2}" left="142.56359863" top="106.72155762" right="302.95659863" bottom="147.87855762"/>
<teximage text="\iff \mathbf{K} = \frac{\sum_{k=1}^N \frac{\mathbf{W}_k / \mathbf{I}_k}{1 / (\mathbf{I}_k)^2}}{\sum_{k=1}^N \frac{1}{1 / (\mathbf{I}_k)^2}}" left="142.22589111" top="148.70774841" right="258.83289111" bottom="191.65474841"/>
<teximage text="\iff \mathbf{K} = \frac{\sum_{k=1}^N \mathbf{W}_k \mathbf{I}_k}{\sum_{k=1}^N (\mathbf{I}_k)^2}" left="142.66387939" top="187.59588623" right="256.17248725" bottom="225.59588623"/>
<teximage text="\text{by linearity of expected value, my actual question is why } \sum_{k=1}^NE[(\mathbf{I}_k)^2] = \sum_{k=1}^N(\mathbf{I}_k)^2?" left="311.52549129" top="-2.28384399" right="596.12817236" bottom="61.96316552"/>
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<text font="Sans" size="12.00000000" x="135.44167763" y="306.34509378" color="#00c0ffff" ts="0" fn="">same for rows</text>
<text font="Sans" size="12.00000000" x="104.01898849" y="279.02986889" color="#00c0ffff" ts="0" fn="">only b), no? and maybe not even completelly</text>
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<text font="Sans" size="12.00000000" x="46.73948682" y="390.13703149" color="#00c0ffff" ts="0" fn="">argument?</text>
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<text font="Sans" size="12.00000000" x="230.99488818" y="465.46172843" color="#00c0ffff" ts="0" fn="">like usual square, right? No along rows and columns? well it
seems clear to me, if it was 4 there would be doubt but it is
not the case</text>
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<text font="Sans" size="12.00000000" x="295.42438097" y="140.58574078" color="#00c0ffff" ts="0" fn="">I should display such one to have an idea what it
looks like</text>
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<text font="Sans" size="12.00000000" x="324.50536302" y="542.32024894" color="#00c0ffff" ts="0" fn="">could reproduce</text>
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<text font="Sans" size="12.00000000" x="321.91570692" y="447.04169660" color="#00c0ffff" ts="0" fn="">decrease in all color channels for all operations</text>
<text font="Sans" size="12.00000000" x="259.42894802" y="535.67410051" color="#00c0ffff" ts="0" fn="">decrease absolutely in all color channels for all operations</text>
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<text font="Sans" size="12.00000000" x="385.93946725" y="438.38111058" color="#ff00ffff" ts="0" fn="">missing space?</text>
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<text font="Sans" size="12.00000000" x="1.99971551" y="501.09241328" color="#00c0ffff" ts="0" fn="">is a method proposed to automatize such identification?</text>
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<text font="Sans" size="12.00000000" x="162.27111816" y="582.22521065" color="#00c0ffff" ts="0" fn="">values?</text>
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<text font="Sans" size="12.00000000" x="39.45615154" y="618.35383644" color="#00c0ffff" ts="0" fn="">what about something like an histogram?</text>
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<text font="Sans" size="12.00000000" x="56.83878603" y="373.35349702" color="#00c0ffff" ts="0" fn="">linear pattern no more used in the paper remaining?</text>
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<text font="Sans" size="12.00000000" x="357.29141293" y="84.89545288" color="#00c0ffff" ts="0" fn="">not what I currently focus on</text>
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<text font="Sans" size="12.00000000" x="389.90006581" y="432.13162644" color="#00c0ffff" ts="0" fn="">what about raw images?</text>
<text font="Sans" size="12.00000000" x="329.83006903" y="407.93830791" color="#00c0ffff" ts="0" fn="">not the same 3 as in 3x3 on page 5, right?
or it means same variance 3 in 2 axes?</text>
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1
articles/Diffraction grating - Wikipedia/Diffraction grating - Wikipedia.pdf.txt
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https://en.wikipedia.org/w/index.php?title=Diffraction_grating&oldid=1221943132

82
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<text font="Sans" size="12.00000000" x="152.70117731" y="709.84895114" color="#00c0ffff" ts="0" fn="">I see the idea</text>
<text font="Sans" size="12.00000000" x="207.01429556" y="707.18455667" color="#00c0ffff" ts="0" fn="">have not checked axis in addition to scale</text>
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rotated
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Is it due to
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introduced by
the rotation?</text>
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due to bricks
it seems cf
sin(x)</text>
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1
articles/Gaussian noise - Wikipedia/Gaussian noise - Wikipedia.pdf.txt
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https://en.wikipedia.org/w/index.php?title=Gaussian_noise&oldid=1164238110

56
articles/Gaussian noise - Wikipedia/Gaussian noise - Wikipedia.xopp.xml
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1
articles/Geometric mean - Wikipedia/Geometric mean - Wikipedia.pdf.txt
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@ -1 +0,0 @@
https://en.wikipedia.org/w/index.php?title=Geometric_mean&oldid=1221446934

55
articles/Geometric mean - Wikipedia/Geometric mean - Wikipedia.xopp.xml
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1
articles/Glare (vision) - Wikipedia/Glare (vision) - Wikipedia.pdf.txt
View File

@ -1 +0,0 @@
https://en.wikipedia.org/w/index.php?title=Glare_(vision)&oldid=1220721645

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