53 Commits

Author SHA1 Message Date
57fc05ed2c Add articles/ipol_style_guide/ 2024-07-24 00:23:39 +02:00
66c1a50d3b Add articles/Hierarchical Data Format - Wikipedia/ 2024-07-24 00:23:01 +02:00
19f8e010e0 Add debugging 2024-07-24 00:22:08 +02:00
7386290e87 Update annotations 2024-07-24 00:21:09 +02:00
025f8d0bee Remove debugging 2024-07-24 00:20:45 +02:00
3a4100b779 Use np.clip 2024-07-04 04:18:13 +02:00
98426db9dd Using matplotlib clipping 2024-07-04 04:11:12 +02:00
d24c883a92 Restore clipping
6f961a46cf
2024-07-04 04:08:36 +02:00
5bc0337dc8 Forcing color map 2024-07-04 04:08:10 +02:00
9d20940cb1 cmap try 2024-07-04 04:03:54 +02:00
6f961a46cf Add clipping 2024-07-04 03:58:30 +02:00
a17ec28cc3 Add color scales 2024-07-04 03:36:36 +02:00
dd7a6892e5 Add articles/Discrete cosine transform - Wikipedia/ 2024-06-28 13:55:24 +02:00
ad5bb767c2 Add articles/JPEG - Wikipedia/ 2024-06-28 12:57:21 +02:00
0fca7c84a0 Support Nikon D90 .nefs 2024-06-17 13:48:21 +00:00
715b5296c9 Compare PRNUs 2024-06-11 17:54:29 +02:00
c2cf139261 Correct pearsonr import 2024-06-11 17:53:34 +02:00
aa5c210539 Comment checkOriginalPoint 2024-06-11 17:52:50 +02:00
2bfedf54ad Remove INTERESTING_VALUES_OF_AXES usage 2024-06-11 17:51:57 +02:00
a9861a761f Use an intermediary variable in getImageColorChannel 2024-06-11 17:35:26 +02:00
0c7108b3ec Make benchmark_load_part_of_images.py production ready 2024-06-11 16:41:43 +02:00
959fb43dd0 Update annotations of Determining Image Origin and Integrity Using Sensor Noise 2024-06-11 16:41:23 +02:00
0d22c36e96 Update annotations of On the Sensor Pattern Noise Estimation in Image Forensics: A Systematic Empirical Evaluation 2024-06-11 16:41:02 +02:00
cb65dbb943 Clean bottom right controls 2024-06-10 15:47:07 +02:00
2c799f12f7 Add comment about purpose of \pdfstringdefDisableCommands 2024-06-10 15:46:48 +02:00
115bab4b4c Add and use getEstimatedPrnu 2024-06-05 16:47:24 +02:00
31da324702 Compute mean PRNU estimate from scratch 2024-06-05 16:41:44 +02:00
be83fcf154 Add PRNU estimation computation 2024-06-05 16:27:40 +02:00
38eca8e65c Add verify_dots.py 2024-06-05 16:23:55 +02:00
fbd7cb01e1 Support lowest correlation in find_consecutive_most_similar_images.py 2024-05-31 16:05:36 +02:00
7f7e78cafb Small changes 2024-05-31 14:07:51 +02:00
4eb8841ebf Clean find_consecutive_most_similar_images.py 2024-05-31 12:12:14 +02:00
0dd21ad34d Add find_consecutive_most_similar_images.py
Using:
- dd80ba940a/utils.py (L53-L55)
- dd80ba940a/chen.py (L81-L89)
2024-05-31 12:10:37 +02:00
93a3078743 Update annotations of `articles/Determining Image Origin and Integrity
Using Sensor Noise/`
2024-05-30 14:54:54 +02:00
a9336958ff Add articles/Expected value - Wikipedia/ 2024-05-30 14:54:41 +02:00
a524d75389 Update annotations of articles/ 2024-05-30 12:34:32 +02:00
ec9f6e7aca Make articles/Determining Image Origin and Integrity Using Sensor Noise/ source permanent 2024-05-29 16:25:53 +02:00
fcb9e84d28 Update Wiener related annotations 2024-05-29 15:57:30 +02:00
30f44e3e47 Update annotations of 2 articles
Commit to illustrate [Benjamin-Loison/xournalpp/issues/33](https://github.com/Benjamin-Loison/xournalpp/issues/33).
2024-05-29 15:27:59 +02:00
12c8da4f11 Add articles/Prior knowledge for pattern recognition - Wikipedia/ 2024-05-29 14:48:31 +02:00
82ed226346 Add articles/Color filter array - Wikipedia/ 2024-05-28 16:54:41 +02:00
81d7a03f1b Add articles/Fisher information - Wikipedia/ 2024-05-28 16:36:10 +02:00
37f35db747 Add articles/Cramér–Rao bound - Wikipedia/ 2024-05-28 16:33:51 +02:00
f9c4ea6387 Add articles/Minimum-variance unbiased estimator - Wikipedia/ 2024-05-28 16:28:27 +02:00
c80d37f464 Update annotations of articles/Determining Image Origin and Integrity Using Sensor Noise 2024-05-28 16:18:29 +02:00
8f48ac775b Add forgotten articles/Depth of field - Wikipedia/ 2024-05-28 15:32:04 +02:00
fa67029adf Add articles/Color balance - Wikipedia/ 2024-05-28 15:31:44 +02:00
0968863ba3 Update annotations of articles/Type I and type II errors - Wikipedia/ 2024-05-28 14:54:10 +02:00
7fc37b91d6 Add articles/Neyman–Pearson lemma - Wikipedia/ 2024-05-28 14:47:06 +02:00
5b33ceb886 Update annotations of articles/preprint-PRNU_WIFS-3/ 2024-05-28 13:32:39 +02:00
9ea9d09b82 Update annotations of articles/preprint-PRNU_WIFS-3/ 2024-05-28 13:23:12 +02:00
e765301ce4 Update annotations articles/A leak in PRNU based source identification. Questioning fingerprint uniqueness/ 2024-05-28 13:22:47 +02:00
8377361a66 Update annotations of articles/Determining Image Origin and Integrity Using Sensor Noise/ 2024-05-28 13:22:27 +02:00
47 changed files with 3021 additions and 94 deletions

View File

@@ -30,12 +30,12 @@ def getPrnuShownAsSuch(size, gaussianNoise = 0):
break break
# Center vertically, especially in the case `HEIGHT` > `WIDTH`. # Center vertically, especially in the case `HEIGHT` > `WIDTH`.
draw.text((0, HEIGHT // 2 - fontSize // 2), TEXT, 255, font = font) 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]) gaussianNoiseNpArray = randomGaussianImage(gaussianNoise, size[::-1])
#prnuShownAsSuch = imageNpArray + gaussianNoiseNpArray #prnuShownAsSuch = imageNpArray + gaussianNoiseNpArray
prnuShownAsSuch = imageNpArray prnuShownAsSuch = imageNpArray
for y in range(HEIGHT): for y in range(HEIGHT):
for x in range(WIDTH): for x in range(WIDTH):
if prnuShownAsSuch[y, x] != 0: if prnuShownAsSuch[y, x] != 0:
prnuShownAsSuch[y, x] += gaussianNoiseNpArray[y, x] prnuShownAsSuch[y, x] = gaussianNoiseNpArray[y, x]
return prnuShownAsSuch return prnuShownAsSuch

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<?xml version="1.0" standalone="no"?>
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<text font="Sans" size="12.00000000" x="46.91696167" y="217.29287720" color="#00c0ffff" ts="0" fn="">why using such method for e paper? is not it the case
for usual displays?</text>
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https://ws2.binghamton.edu/fridrich/Research/DoubleColumnFinal.pdf https://web.archive.org/web/20221208035521id_/https://ws2.binghamton.edu/fridrich/Research/DoubleColumnFinal.pdf

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not interesting occurrences of source</text>
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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="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>
<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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@@ -83,11 +791,36 @@ unclear</text>
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<text font="Sans" size="12.00000000" x="49.36018785" y="425.39423025" color="#00c0ffff" ts="0" fn="">not what I currently focus on</text>
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@@ -103,10 +836,24 @@ unclear</text>
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<text font="Sans" size="12.00000000" x="452.09175872" y="378.60406499" color="#00c0ffff" ts="0" fn="">see (34)</text>
<text font="Sans" size="12.00000000" x="479.99473016" y="361.09989525" color="#00c0ffff" ts="0" fn="">see (26)</text>
<text font="Sans" size="12.00000000" x="308.01572735" y="389.19037438" color="#00c0ffff" ts="0" fn="">see (18)</text>
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<text font="Sans" size="12.00000000" x="365.57879639" y="565.35534668" color="#00c0ffff" ts="0" fn="">according to Marina demo it is sPCE</text>
<text font="Sans" size="12.00000000" x="298.39611816" y="609.24667358" color="#00c0ffff" ts="0" fn="">what shape and size typically? 11 sized square
chose Marina</text>
<text font="Sans" size="12.00000000" x="311.70404053" y="544.69454956" color="#00c0ffff" ts="0" fn="">why (0, 0) and not center? more generally
how explain this formula?
well with Marina np.roll there is such shift</text>
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<text font="Sans" size="11.00000000" x="312.11756340" y="438.97829411" color="#00c0ffff" ts="0" fn="">do they provide the MATLAB code? <text font="Sans" size="11.00000000" x="312.11756340" y="438.97829411" color="#00c0ffff" ts="0" fn="">do they provide the MATLAB code?
No interesting occurrences of code, source (code), git, No interesting occurrences of code, source (code), git,
implementation, web</text> implementation, web</text>
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@@ -0,0 +1 @@
https://en.wikipedia.org/w/index.php?title=Prior_knowledge_for_pattern_recognition&oldid=1166323826

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<text font="Sans" size="12.00000000" x="304.24145269" y="413.32398928" color="#00c0ffff" ts="0" fn="">?</text>
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<text font="Sans" size="12.00000000" x="303.69464111" y="223.37280273" color="#00c0ffff" ts="0" fn="">it seems that
CCN(P, R) = sqrt(sPCE(P, R)) * sign(sPCE(P, R))</text>
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<text font="Sans" size="12.00000000" x="195.21855164" y="400.61158752" color="#00c0ffff" ts="0" fn="">unclear meaning</text>
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<text font="Sans" size="12.00000000" x="210.00827026" y="434.63031006" color="#00c0ffff" ts="0" fn="">why however?</text>
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<text font="Sans" size="12.00000000" x="315.56872476" y="138.42515356" color="#00c0ffff" ts="0" fn="">as a shift of 1 pixel makes the thing incorrect
cropping looks incorrect to me</text>
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<text font="Sans" size="12.00000000" x="317.81186368" y="307.05381362" color="#00c0ffff" ts="0" fn="">means quadratic complexity?</text>
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View File

@@ -154,11 +154,30 @@
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@@ -174,7 +193,33 @@
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View File

@@ -24,7 +24,7 @@ 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. # 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. # 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 PRNU_FACTOR = 0.01
NOISE_FACTOR = 0.1 NOISE_FACTOR = 0.2
np.random.seed(0) np.random.seed(0)
@@ -34,7 +34,12 @@ SPLIT_N_X_N_S = [1, 2, 4]
fig, axes = plt.subplots(2, 4) fig, axes = plt.subplots(2, 4)
fig.suptitle('PRNU estimation with different number of images having Gaussian noise and Gaussian noised PRNU') fig.suptitle('PRNU estimation with different number of images having Gaussian noise and Gaussian noised PRNU')
def axisImShow(axis, im):
imShow = axis.imshow(im)
plt.colorbar(imShow, label = 'Intensity', ax = axis, orientation = 'horizontal')
for splitNXNIndex, splitNXN in enumerate(SPLIT_N_X_N_S): for splitNXNIndex, splitNXN in enumerate(SPLIT_N_X_N_S):
print(f'{splitNXN=}')
IMAGE_SIZE_SHAPE = [dimension // splitNXN for dimension in (704, 469)] IMAGE_SIZE_SHAPE = [dimension // splitNXN for dimension in (704, 469)]
#prnuNpArray = 255 * randomGaussianImage(scale = PRNU_FACTOR, size = IMAGE_SIZE_SHAPE) #prnuNpArray = 255 * randomGaussianImage(scale = PRNU_FACTOR, size = IMAGE_SIZE_SHAPE)
@@ -55,10 +60,13 @@ for splitNXNIndex, splitNXN in enumerate(SPLIT_N_X_N_S):
m = IMAGE_SIZE_SHAPE[1] m = IMAGE_SIZE_SHAPE[1]
n = IMAGE_SIZE_SHAPE[0] 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)] imageWithoutPrnuNpArrayTiles = [imageWithoutPrnuNpArray[x * m : (x + 1) * m, y * n : (y + 1) * n] for x in range(splitNXN) for y in range(splitNXN)]
#print(f'{m=} {n=} {0 * m=} {0 * (m + 1)=}')
#print([imageWithoutPrnuNpArrayTile.shape for imageWithoutPrnuNpArrayTile in imageWithoutPrnuNpArrayTiles])
for imageWithoutPrnuNpArrayTile in imageWithoutPrnuNpArrayTiles: for imageWithoutPrnuNpArrayTile in imageWithoutPrnuNpArrayTiles:
#print(imageWithoutPrnuNpArrayTile.shape, tuple(IMAGE_SIZE_SHAPE[::-1])) #print(imageWithoutPrnuNpArrayTile.shape, tuple(IMAGE_SIZE_SHAPE[::-1]))
#if imageWithoutPrnuNpArrayTile.shape != tuple(IMAGE_SIZE_SHAPE[::-1]): #if imageWithoutPrnuNpArrayTile.shape != tuple(IMAGE_SIZE_SHAPE[::-1]):
# exit(1)
# continue # continue
imageNoise = randomGaussianImage(scale = 255 * NOISE_FACTOR, size = imageWithoutPrnuNpArrayTile.shape) imageNoise = randomGaussianImage(scale = 255 * NOISE_FACTOR, size = imageWithoutPrnuNpArrayTile.shape)
imageWithPrnuNpArray = imageWithoutPrnuNpArrayTile + prnuNpArray + imageNoise imageWithPrnuNpArray = imageWithoutPrnuNpArrayTile + prnuNpArray + imageNoise
@@ -67,16 +75,19 @@ for splitNXNIndex, splitNXN in enumerate(SPLIT_N_X_N_S):
axis = axes[0] axis = axes[0]
axis[0].set_title('First image without noise') axis[0].set_title('First image without noise')
axis[0].imshow(imageWithoutPrnuNpArrayTile) axisImShow(axis[0], imageWithoutPrnuNpArrayTile)
axis[1].set_title('Actual Gaussian noised PRNU') axis[1].set_title('First image Gaussian noise')
axis[1].imshow(prnuNpArray) axisImShow(axis[1], imageNoise)
axis[2].set_title('F. i. with G. n.') axis[2].set_title('First image with Gaussian noise\nClipped between 0 and 255')
axis[2].imshow(imageWithoutPrnuNpArray + imageNoise) axisImShow(axis[2], np.clip(imageWithoutPrnuNpArray + imageNoise, 0, 255))
axis[3].set_title('F. i. with G. n. and PRNU') axis[3].set_title('Actual Gaussian noised PRNU')
axis[3].imshow(imageWithoutPrnuNpArray + prnuNpArray + imageNoise) axisImShow(axis[3], prnuNpArray)
axes[1][0].set_title('First image with Gaussian noise and PRNU\nClipped between 0 and 255')
axisImShow(axes[1][0], np.clip(imageWithoutPrnuNpArray + prnuNpArray + imageNoise, 0, 255))
isFirstImage = False isFirstImage = False
#assert all([isIn256Range(extreme) for extreme in [imageWithPrnuNpArray.max(), imageWithPrnuNpArray.min()]]), 'Adding the PRNU resulted in out of 256 bounds image' #assert all([isIn256Range(extreme) for extreme in [imageWithPrnuNpArray.max(), imageWithPrnuNpArray.min()]]), 'Adding the PRNU resulted in out of 256 bounds image'
@@ -91,10 +102,8 @@ for splitNXNIndex, splitNXN in enumerate(SPLIT_N_X_N_S):
rms = rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray, True) rms = rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray, True)
title = f'RMS with actual PRNU: {rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray):.4f}\n(normalized RMS: {rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray, True):.4f})' title = f'RMS with actual PRNU: {rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray):.4f}\n(normalized RMS: {rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray, True):.4f})'
axis = axes[1] axis = axes[1]
axis[splitNXNIndex].set_title(f'Number of images: {len(imagesPrnuEstimateNpArray)}\n{title}') axis[splitNXNIndex + 1].set_title(f'PRNU estimation\nwith {len(imagesPrnuEstimateNpArray)} images')#\n{title}
axis[splitNXNIndex].imshow(cameraPrnuEstimateNpArray) axisImShow(axis[splitNXNIndex + 1], cameraPrnuEstimateNpArray)
axes[1][3].axis('off')
plt.tight_layout() plt.tight_layout()
plt.show() plt.show()

View File

@@ -1,6 +1,6 @@
#!/usr/bin/env python #!/usr/bin/env python
from utils import getColorChannel, Color from utils import getColorChannel, Color, isARawImage
import os import os
from tqdm import tqdm from tqdm import tqdm
import numpy as np import numpy as np
@@ -19,7 +19,7 @@ class Operation(Enum):
def __str__(self): def __str__(self):
return self.name.lower() return self.name.lower()
OPERATION = Operation.LOAD_NPY OPERATION = Operation.SAVE
RESOLUTION = 100 RESOLUTION = 100
print(f'{OPERATION = }') print(f'{OPERATION = }')
if OPERATION == Operation.LOAD_NPY: if OPERATION == Operation.LOAD_NPY:
@@ -32,7 +32,7 @@ def getNumpyFilePath(imageFilePath, color):
for camera in tqdm(IMAGES_CAMERAS_FOLDER, 'Camera'): for camera in tqdm(IMAGES_CAMERAS_FOLDER, 'Camera'):
imagesCameraFolder = IMAGES_CAMERAS_FOLDER[camera] imagesCameraFolder = IMAGES_CAMERAS_FOLDER[camera]
for file in tqdm(os.listdir(imagesCameraFolder), 'Image'): for file in tqdm(os.listdir(imagesCameraFolder), 'Image'):
if file.endswith('.NEF') or file.endswith('.ARW'): if isARawImage(file):
#print(file) #print(file)
imageFilePath = f'{imagesCameraFolder}/{file}' imageFilePath = f'{imagesCameraFolder}/{file}'
for color in Color: for color in Color:

View File

@@ -15,21 +15,22 @@ def getImageFilePath(imageIndex):
def getImageColorChannel(imageIndex, color): def getImageColorChannel(imageIndex, color):
imageFilePath = getImageFilePath(imageIndex) imageFilePath = getImageFilePath(imageIndex)
return getColorChannel(imageFilePath, color).astype(np.int32) colorChannel = getColorChannel(imageFilePath, color)
return colorChannel
def crop(image, interestingPosition, yRange, xRange):
return image[interestingPosition[0] - yRange: interestingPosition[0] + yRange, interestingPosition[1] - xRange: interestingPosition[1] + xRange]
# Could leverage crop already here. # Could leverage crop already here.
colorChannels = [getImageColorChannel(imageIndex, COLOR) for imageIndex in tqdm(IMAGE_INDEX_RANGE)] colorChannels = [crop(getImageColorChannel(imageIndex, COLOR), INTERESTING_POSITION, Y_RANGE, X_RANGE).astype(np.int32) for imageIndex in tqdm(IMAGE_INDEX_RANGE)]
meanColorChannels = np.mean(colorChannels, axis = 0) meanColorChannels = np.mean(colorChannels, axis = 0)
estimatedPrnus = [colorChannel - meanColorChannels for colorChannel in tqdm(colorChannels)] estimatedPrnus = [colorChannel - meanColorChannels for colorChannel in tqdm(colorChannels)]
estimatedPrnu = np.mean(estimatedPrnus, axis = 0) estimatedPrnu = np.mean(estimatedPrnus, axis = 0)
print(estimatedPrnu.shape) print(estimatedPrnu.shape)
def crop(image, interestingPosition, yRange, xRange):
return image[interestingPosition[0] - yRange: interestingPosition[0] + yRange, interestingPosition[1] - xRange: interestingPosition[1] + xRange]
#colorChannel = crop(multipleColorsImage, ORIGINAL_INTERESTING_POSITION, Y_RANGE, X_RANGE) #colorChannel = crop(multipleColorsImage, ORIGINAL_INTERESTING_POSITION, Y_RANGE, X_RANGE)
colorChannelToDisplay = estimatedPrnu colorChannelToDisplay = estimatedPrnu
colorChannelToDisplay = crop(colorChannelToDisplay, INTERESTING_POSITION, Y_RANGE, X_RANGE) #colorChannelToDisplay = crop(colorChannelToDisplay, INTERESTING_POSITION, Y_RANGE, X_RANGE)
print(colorChannelToDisplay.shape) print(colorChannelToDisplay.shape)
plt.imshow(colorChannelToDisplay) plt.imshow(colorChannelToDisplay)
plt.show() plt.show()

View File

@@ -31,11 +31,13 @@ for axis in Axis:
axisProfile = PROFILES_OF_AXES[axis] axisProfile = PROFILES_OF_AXES[axis]
colorsOfAxes[axis] = plt.plot(axisProfile, label = axis)[0].get_color() colorsOfAxes[axis] = plt.plot(axisProfile, label = axis)[0].get_color()
'''
for axis in Axis: for axis in Axis:
for interestingValueOfAxisIndex, interestingValueOfAxis in enumerate(INTERESTING_VALUES_OF_AXES[axis]): for interestingValueOfAxisIndex, interestingValueOfAxis in enumerate(INTERESTING_VALUES_OF_AXES[axis]):
linestyle = ':' if interestingValueOfAxisIndex % 2 == 0 else '--' linestyle = ':' if interestingValueOfAxisIndex % 2 == 0 else '--'
label = f'{axis} local {"minimum" if interestingValueOfAxisIndex == 0 else "maximum"}' if interestingValueOfAxisIndex <= 1 else None label = f'{axis} local {"minimum" if interestingValueOfAxisIndex == 0 else "maximum"}' if interestingValueOfAxisIndex <= 1 else None
plt.axvline(interestingValueOfAxis, color = colorsOfAxes[axis], alpha = 0.3, linestyle = linestyle, label = label) plt.axvline(interestingValueOfAxis, color = colorsOfAxes[axis], alpha = 0.3, linestyle = linestyle, label = label)
'''
def flattenDictValues(dict_): def flattenDictValues(dict_):
return itertools.chain(*dict_.values()) return itertools.chain(*dict_.values())
@@ -43,6 +45,7 @@ def flattenDictValues(dict_):
# TODO: How to avoid hardcoding the default color being `black`? # TODO: How to avoid hardcoding the default color being `black`?
MATPLOTLIB_DEFAULT_COLOR = 'black' MATPLOTLIB_DEFAULT_COLOR = 'black'
'''
allInterestingValuesOfAxes = list(set(flattenDictValues(INTERESTING_VALUES_OF_AXES))) allInterestingValuesOfAxes = list(set(flattenDictValues(INTERESTING_VALUES_OF_AXES)))
ticks = list(plt.xticks()[0]) + allInterestingValuesOfAxes ticks = list(plt.xticks()[0]) + allInterestingValuesOfAxes
plt.xticks(ticks) plt.xticks(ticks)
@@ -62,6 +65,7 @@ allInterestingIntensitiesOfAxes = list(flattenDictValues(INTERESTING_PROFILES_OF
allInterestingIntensitiesOfAxesMin = min(allInterestingIntensitiesOfAxes) allInterestingIntensitiesOfAxesMin = min(allInterestingIntensitiesOfAxes)
allInterestingIntensitiesOfAxesMax = max(allInterestingIntensitiesOfAxes) allInterestingIntensitiesOfAxesMax = max(allInterestingIntensitiesOfAxes)
plt.ylim(allInterestingIntensitiesOfAxesMin, allInterestingIntensitiesOfAxesMax) plt.ylim(allInterestingIntensitiesOfAxesMin, allInterestingIntensitiesOfAxesMax)
'''
plt.legend() plt.legend()
plt.show() plt.show()

View File

@@ -2,6 +2,8 @@ import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from tqdm import tqdm from tqdm import tqdm
# TODO: seems to avoid some intermediary values resulting in visible circles
A = -1.076923838846692e-06 A = -1.076923838846692e-06
B = 0.0010539307456559996 B = 0.0010539307456559996
C = 42 C = 42
@@ -27,9 +29,12 @@ for y in tqdm(range(SHAPE[0])):
def checkOriginalPoint(point): def checkOriginalPoint(point):
print(f'distance = {getDistance(*point)} intensity = {image[tuple(point)]}') print(f'distance = {getDistance(*point)} intensity = {image[tuple(point)]}')
for point in [CENTER, [0, 0], [CENTER[0], int(CENTER[1] + getDistance(0, 0) / 2)]]: #for point in [CENTER, [0, 0], [CENTER[0], int(CENTER[1] + getDistance(0, 0) / 2)]]:
checkOriginalPoint(point) # checkOriginalPoint(point)
pad = (SHAPE[1] - SHAPE[0]) // 2
cropImage = image[:, pad:-pad]
#plt.imshow(image, cmap = 'grey') #plt.imshow(image, cmap = 'grey')
plt.imsave('vignetting.png', image * 2.55, cmap = 'grey', vmin = 0, vmax = 255) plt.imsave('vignetting_odd.png', image * 2.55, cmap = 'grey', vmin = 0, vmax = 255)
plt.show() plt.show()

View File

@@ -3,13 +3,15 @@ import numpy as np
from scipy import fftpack from scipy import fftpack
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
image = np.array(Image.open('circle.png').convert('L'))# / 255 IMAGE_FILE_NAME = 'manual_tests/horizontal_ellipse'
image = np.array(Image.open(f'{IMAGE_FILE_NAME}.png').convert('L'))# / 255
# fft of image # fft of image
fft1 = fftpack.fftshift(fftpack.fft2(image)) fft1 = fftpack.fftshift(fftpack.fft2(image))
# save the image # save the image
#toDisplay = np.angle(fft1)
toDisplay = np.log10(1 + abs(fft1)) toDisplay = np.log10(1 + abs(fft1))
plt.imshow(toDisplay) plt.imshow(toDisplay)
plt.show() plt.show()
plt.imsave('ifft1.png', toDisplay) #plt.imsave(f'{IMAGE_FILE_NAME}_fft.png', toDisplay)

View File

@@ -0,0 +1,48 @@
import os
from tqdm import tqdm
import numpy as np
import matplotlib.pyplot as plt
import sys
sys.path.insert(0, '../')
from utils import isARawImage, Color, getColorChannel, mergeSingleColorChannelImagesAccordingToBayerFilter, iterativeMean, getPearsonCorrelationDistance
FOLDERS = {
'Rafael 23/04/24': '../rafael/230424',
'RAISE flat-field': '../flat-field/nef',
'RAISE non-flat-field': '../non-flat-field/nef',
}
def getImageMergedColorChannels(filePath):
imageColorChannels = {color: getColorChannel(filePath, color) for color in Color}
imageMergedColorChannels = mergeSingleColorChannelImagesAccordingToBayerFilter(imageColorChannels)
return imageMergedColorChannels
def getEstimatedPrnu(folder):
meanImages = iterativeMean()
filePaths = [f'{folder}/{file}' for file in os.listdir(folder) if isARawImage(file)]
for filePath in tqdm(filePaths, 'Compute mean image'):
imageMergedColorChannels = getImageMergedColorChannels(filePath)
meanImages.add(imageMergedColorChannels)
# Is not there anything clever than this to do? See [Robust_image_source_identification_on_modern_smartphones/issues/72](https://gitea.lemnoslife.com/Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones/issues/72).
estimatedPrnu = iterativeMean()
for filePath in tqdm(filePaths, 'Compute estimated PRNU'):
imageMergedColorChannels = getImageMergedColorChannels(filePath)
estimatedPrnu.add(imageMergedColorChannels - meanImages.mean)
return estimatedPrnu.mean
estimatedPrnus = {folderName: getEstimatedPrnu(folder) for folderName, folder in FOLDERS.items()}
# TODO: largest common crop
print(f'{corr(estimatedPrnus["Rafael 23/04/24"], estimatedPrnus["Rafael 23/04/24"])=}')
print(f'{corr(estimatedPrnus["Rafael 23/04/24"], estimatedPrnus["RAISE flat-field"])=}')
print(f'{corr(estimatedPrnus["RAISE flat-field"], estimatedPrnus["Rafael 23/04/24"])=}')
print(f'{corr(estimatedPrnus["Rafael 23/04/24"], estimatedPrnus["RAISE non-flat-field"])=}')
print(f'{corr(estimatedPrnus["RAISE flat-field"], estimatedPrnus["RAISE non-flat-field"])=}')
plt.imshow(estimatedPrnus['RAISE flat-field'])
plt.show()

View File

@@ -0,0 +1,48 @@
#!/usr/bin/python3
import os
from utils import getColorChannel, Color, mergeSingleColorChannelImagesAccordingToBayerFilter, isARawImage
from tqdm import tqdm
import numpy as np
folder = 'flat-field/NEF'
# Section I. of *Determining Image Origin and Integrity Using Sensor Noise*.
def dotProduct(X, Y):
return np.multiply(X, Y).sum()
# Section I. of *Determining Image Origin and Integrity Using Sensor Noise*.
def corr(X, Y):
XMinusItsMean = X - X.mean()
YMinusItsMean = Y - Y.mean()
return dotProduct(XMinusItsMean, YMinusItsMean) / (np.linalg.norm(XMinusItsMean) * np.linalg.norm(YMinusItsMean))
def crop(image, yRange, xRange):
interestingPosition = np.array(image.shape) // 2
return image[interestingPosition[0] - yRange: interestingPosition[0] + yRange, interestingPosition[1] - xRange: interestingPosition[1] + xRange]
lastMergedSingleColorChannelImage = None
lastCorrelation = None
# Supports both highest and lowest.
extremeCorrelation = None
extremeCorrelationFile = None
files = [file for file in sorted(os.listdir(folder)) if isARawImage(file)]
for file in tqdm(files, 'File'):
colorChannels = {}
for color in Color:
colorChannel = getColorChannel(f'{folder}/{file}', color)
colorChannel = crop(colorChannel, 200, 200)
colorChannels[color] = colorChannel
mergedSingleColorChannelImage = mergeSingleColorChannelImagesAccordingToBayerFilter(colorChannels)
if lastMergedSingleColorChannelImage is not None:
correlation = corr(mergedSingleColorChannelImage, lastMergedSingleColorChannelImage)
if extremeCorrelation is None or correlation < extremeCorrelation:
extremeCorrelation = correlation
extremeCorrelationFile = file
lastMergedSingleColorChannelImage = mergedSingleColorChannelImage
print(f'{extremeCorrelationFile} (and previous file) have the lowest correlation with a correlation of {extremeCorrelation}')

View File

@@ -10,13 +10,13 @@ import builtins as __builtin__
from scipy.ndimage import gaussian_filter from scipy.ndimage import gaussian_filter
import os import os
from skimage.restoration import estimate_sigma from skimage.restoration import estimate_sigma
from scipy import fftpack from scipy import fftpack, stats
import imageio import imageio
class Color(Enum): class Color(Enum):
RED = auto() RED = auto()
GREEN_RIGHT = auto() GREEN_RIGHT = auto()
GREEN_BOTTOM = auto() GREEN_LEFT = auto()
BLUE = auto() BLUE = auto()
def __str__(self): def __str__(self):
@@ -44,7 +44,7 @@ class Distance(Enum):
return self.name.replace('_', '').title() return self.name.replace('_', '').title()
def getPearsonCorrelationDistance(firstImage, secondImage): def getPearsonCorrelationDistance(firstImage, secondImage):
pearsonCorrelation = scipy.stats.pearsonr(firstImage.flatten(), secondImage.flatten()).statistic pearsonCorrelation = stats.pearsonr(firstImage.flatten(), secondImage.flatten()).statistic
if pearsonCorrelation < 0: if pearsonCorrelation < 0:
print('Negative Pearson correlation, investigate further!') print('Negative Pearson correlation, investigate further!')
exit(1) exit(1)
@@ -117,29 +117,44 @@ RAW_IMAGE_FILE_EXTENSIONS = [
'nef', 'nef',
] ]
SUPPORTED_RAW_PATTERNS = [
# RG
# GB
[[0, 1],
[3, 2]],
# GB
# RG
[[3, 2],
[0, 1]],
]
def getRawColorChannel(raw, color): def getRawColorChannel(raw, color):
colorDesc = raw.color_desc.decode('ascii') colorDesc = raw.color_desc.decode('ascii')
assert colorDesc == 'RGBG' assert colorDesc == 'RGBG'
assert np.array_equal(raw.raw_pattern, np.array([[0, 1], [3, 2]], dtype = np.uint8)) assert any(np.array_equal(raw.raw_pattern, np.array(supportedRawPattern, dtype = np.uint8)) for supportedRawPattern in SUPPORTED_RAW_PATTERNS)
# RG
# GB
rawImageVisible = raw.raw_image_visible.copy() rawImageVisible = raw.raw_image_visible.copy()
redRawImageVisible = rawImageVisible[::2, ::2] topLeftRawImageVisible = rawImageVisible[::2, ::2]
greenRightRawImageVisible = rawImageVisible[::2, 1::2] topRightRawImageVisible = rawImageVisible[::2, 1::2]
greenBottomRawImageVisible = rawImageVisible[1::2, ::2] bottomLeftRawImageVisible = rawImageVisible[1::2, ::2]
blueRawImageVisible = rawImageVisible[1::2, 1::2] bottomRightRawImageVisible = rawImageVisible[1::2, 1::2]
match color: if np.array_equal(raw.raw_pattern, np.array(SUPPORTED_RAW_PATTERNS[0], dtype = np.uint8)):
case Color.RED: imageNpArrays = {
imageNpArray = redRawImageVisible Color.RED: topLeftRawImageVisible,
case Color.GREEN_RIGHT: Color.GREEN_RIGHT: topRightRawImageVisible,
imageNpArray = greenRightRawImageVisible Color.GREEN_LEFT: bottomLeftRawImageVisible,
case Color.GREEN_BOTTOM: Color.BLUE: bottomRightRawImageVisible,
imageNpArray = greenBottomRawImageVisible }
case Color.BLUE: else: # elif np.array_equal(raw.raw_pattern, np.array(SUPPORTED_RAW_PATTERNS[1], dtype = np.uint8)):
imageNpArray = blueRawImageVisible imageNpArrays = {
Color.GREEN_LEFT: topLeftRawImageVisible,
Color.BLUE: topRightRawImageVisible,
Color.RED: bottomLeftRawImageVisible,
Color.GREEN_RIGHT: bottomRightRawImageVisible,
}
imageNpArray = imageNpArrays[color]
return imageNpArray return imageNpArray
def isARawImage(imageFilePath): def isARawImage(imageFilePath):
@@ -148,7 +163,7 @@ def isARawImage(imageFilePath):
def getColorChannel(imageFilePath, color): def getColorChannel(imageFilePath, color):
if isARawImage(imageFilePath): if isARawImage(imageFilePath):
numpyFilePath = f'{imageFilePath}.{color}.npy' numpyFilePath = f'{imageFilePath}.{color}.npy'
if False and os.path.isfile(numpyFilePath): if os.path.isfile(numpyFilePath):
imageNpArray = np.load(numpyFilePath) imageNpArray = np.load(numpyFilePath)
else: else:
with rawpy.imread(imageFilePath) as raw: with rawpy.imread(imageFilePath) as raw:
@@ -161,7 +176,6 @@ def getColorChannel(imageFilePath, color):
def escapeFilePath(filePath): def escapeFilePath(filePath):
return filePath.replace('/', '_') return filePath.replace('/', '_')
def getNewIndex(index, offset): def getNewIndex(index, offset):
newIndex = (index - offset) * 2 + offset newIndex = (index - offset) * 2 + offset
return newIndex return newIndex
@@ -179,7 +193,7 @@ def mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages
''' '''
multipleColorsImage[::2,::2] = singleColorChannelImages[Color.RED] multipleColorsImage[::2,::2] = singleColorChannelImages[Color.RED]
multipleColorsImage[1::2,::2] = singleColorChannelImages[Color.GREEN_RIGHT] multipleColorsImage[1::2,::2] = singleColorChannelImages[Color.GREEN_RIGHT]
multipleColorsImage[::2,1::2] = singleColorChannelImages[Color.GREEN_BOTTOM] multipleColorsImage[::2,1::2] = singleColorChannelImages[Color.GREEN_LEFT]
multipleColorsImage[1::2,1::2] = singleColorChannelImages[Color.BLUE] multipleColorsImage[1::2,1::2] = singleColorChannelImages[Color.BLUE]
return multipleColorsImage return multipleColorsImage

View File

@@ -1,5 +1,15 @@
\documentclass{beamer} \documentclass{beamer}
% Only show at bottom right the current page number over the total number of pages.
\setbeamertemplate{navigation symbols}
{
\usebeamerfont{footline}
\usebeamercolor[fg]{footline}
\hspace{1em}
\insertframenumber/\inserttotalframenumber
}
% To avoid warning `Token not allowed in a PDF string (Unicode):(hyperref) removing `\\'`
\pdfstringdefDisableCommands{% \pdfstringdefDisableCommands{%
\def\\{}% \def\\{}%
} }