Add articles/ipol_style_guide/

6f961a46cf

algorithms/image_utils/image_utils.py

@@ -30,12 +30,12 @@ def getPrnuShownAsSuch(size, gaussianNoise = 0):
             break
     # Center vertically, especially in the case `HEIGHT` > `WIDTH`.
     draw.text((0, HEIGHT // 2 - fontSize // 2), TEXT, 255, font = font)
-    imageNpArray = np.array(imagePil)
+    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
+                prnuShownAsSuch[y, x] = gaussianNoiseNpArray[y, x]
+    return prnuShownAsSuch

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

@@ -0,0 +1 @@
+https://ieeexplore.ieee.org/abstract/document/4218950

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

@@ -0,0 +1 @@
+https://web.archive.org/web/20240521094929/https://arxiv.org/pdf/2112.02858

articles/A comparison study of CNN denoisers on PRNU extraction/A comparison study of CNN denoisers on PRNU extraction.xopp.xml

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+      <text font="Sans" size="12.00000000" x="406.16245813" y="246.48769518" color="#00c0ffff" ts="0" fn="">what's the finding here?</text>
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+      <text font="Sans" size="12.00000000" x="373.07061874" y="282.80741315" color="#00c0ffff" ts="0" fn="">not identical somehow?</text>
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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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+https://web.archive.org/web/20240521084451/https://link.springer.com/content/pdf/10.1186/1687-5281-2014-19.pdf

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articles/Clutter (radar) - Wikipedia/Clutter (radar) - Wikipedia.pdf.txt

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articles/Color balance - Wikipedia/Color balance - Wikipedia.pdf.txt

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articles/Color filter array - Wikipedia/Color filter array - Wikipedia.pdf.txt

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articles/Colour banding - Wikipedia/Colour banding - Wikipedia.pdf.txt

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articles/Confusion matrix - Wikipedia/Confusion matrix - Wikipedia.pdf.txt

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articles/Constant false alarm rate - Wikipedia/Constant false alarm rate - Wikipedia.pdf.txt

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articles/Convolution - Wikipedia/Convolution - Wikipedia.pdf.txt

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articles/Covariance - Wikipedia/Covariance - Wikipedia.pdf.txt

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articles/Cramér–Rao bound - Wikipedia/Cramér–Rao bound - Wikipedia.pdf.txt

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articles/Cross-correlation - Wikipedia/Cross-correlation - Wikipedia.xopp.xml

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articles/Determining Image Origin and Integrity Using Sensor Noise/Determining Image Origin and Integrity Using Sensor Noise.pdf.txt

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+pre-process PRNU estimation:
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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"/>
+      <text font="Sans" size="12.00000000" x="236.42767334" y="609.71469116" color="#00c0ffff" ts="0" fn="">looks symetric</text>
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+      <text font="Sans" size="12.00000000" x="192.28253174" y="79.48443604" color="#00c0ffff" ts="0" fn="">?</text>
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+      <text font="Sans" size="12.00000000" x="206.68130493" y="166.50595093" color="#00c0ffff" ts="0" fn="">two??</text>
+      <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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+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">47.97225952 509.77767944 172.58543579 509.77767944</stroke>
+      <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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+      <stroke tool="pen" ts="0" fn="" color="#00c0ffff" width="1.41000000">194.06729126 560.01876831 201.72512817 548.12371826</stroke>
+      <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"/>
+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">57.79257202 685.46777344 194.09613044 685.46777344</stroke>
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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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+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">536.76416016 124.47381592 558.82409668 126.14575195</stroke>
+      <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>
+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">474.84166169 699.74548088 561.62416116 699.74548088</stroke>
+      <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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+  </page>
+  <page width="595.27559100" height="841.88976400">
+    <background type="solid" color="#ffffffff" style="plain"/>
+    <layer>
+      <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="111.73567338" y="193.13123467" color="#00c0ffff" ts="0" fn="">fixe</text>
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+      <stroke tool="highlighter" color="#00c0ff7f" width="8.50000000" fill="128">48.74759390 295.11279902 150.13120123 295.11279902</stroke>
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+      <stroke tool="highlighter" color="#00c0ff7f" width="8.50000000" fill="128">49.06749883 329.07175111 105.61027144 329.07175111</stroke>
+      <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>
+      <stroke tool="highlighter" color="#00c0ff7f" width="8.50000000" fill="128">206.91503099 415.53977777 284.60588323 415.53977777</stroke>
+      <stroke tool="highlighter" color="#00c0ff7f" width="8.50000000" fill="128">48.14256400 427.59472555 252.23296976 427.59472555</stroke>
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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="80.33303505" y="597.63020665" color="#00c0ffff" ts="0" fn="">?</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>
+      <teximage text="corr?\\ \text{The only other occurrence is in VI. D.}" left="327.64978027" top="349.43379211" right="447.58863276" bottom="370.27279020"/>
+      <text font="Sans" size="12.00000000" x="462.96081543" y="350.33961487" color="#00c0ffff" ts="0" fn="">I would say so</text>
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+      <text font="Sans" size="12.00000000" x="138.43141666" y="24.27757578" color="#00c0ffff" ts="0" fn="">may not be a priority yet</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="493.49877514" y="457.36006076" color="#00c0ffff" ts="0" fn="">should investigate</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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+      <text font="Sans" size="12.00000000" x="49.85240131" y="299.48938750" color="#00c0ffff" ts="0" fn="">maybe not what I currently focus on</text>
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articles/Diffraction grating - Wikipedia/Diffraction grating - Wikipedia.pdf.txt

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articles/Digital Camera Identification from Sensor Pattern Noise/Digital Camera Identification from Sensor.pdf.txt

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articles/Distinguishing Digital Image Origin via Higher Order Statistics - Large_scale_test_of_sensor_fingerprint_camera_iden/Distinguishing Digital Image Origin via Higher Order Statistics - Large_scale_test_of_sensor_fingerprint_camera_iden.pdf.txt

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articles/FloreView_An_Image_and_Video_Dataset_for_Forensic_Analysis/FloreView_An_Image_and_Video_Dataset_for_Forensic_Analysis.pdf.txt

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

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articles/Fourier transforms of images plus.maths.org/Fourier transforms of images plus.maths.org.pdf.txt

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+https://web.archive.org/web/20230927155231/https://plus.maths.org/content/fourier-transforms-images

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articles/Geometric mean - Wikipedia/Geometric mean - Wikipedia.pdf.txt

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+https://en.wikipedia.org/w/index.php?title=Geometric_mean&oldid=1221446934

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articles/Glare (vision) - Wikipedia/Glare (vision) - Wikipedia.pdf.txt

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articles/Gradient-domain image processing - Wikipedia/Gradient-domain image processing - Wikipedia.pdf.txt

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articles/HOW TO READ SMARTPHONE CAMERA SPECS - Inquisitive Universe/HOW TO READ SMARTPHONE CAMERA SPECS - Inquisitive Universe.pdf.txt

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articles/Hierarchical Data Format - Wikipedia/Hierarchical Data Format - Wikipedia.pdf.txt

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