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Benjamin_Loison:evaluation
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compare: Benjamin_Loison:prnu_written_as_such
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Benjamin_Loison:master Benjamin_Loison:prnu_written_as_such
Benjamin_Loison:evaluation

1 Commits
9ea9d09b82 ... 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
242 changed files with 42 additions and 9951 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/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

101
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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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articles/Convolution - Wikipedia/Convolution - Wikipedia.pdf.txt
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articles/Focal length - Wikipedia/Focal length - Wikipedia.pdf.txt
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