From 35eaf951fa21733b51c9fb4adeb3924dd8e848ef Mon Sep 17 00:00:00 2001 From: Benjamin Loison Date: Tue, 5 Mar 2024 17:01:05 +0100 Subject: [PATCH] Update `On the Sensor Pattern Noise Estimation in Image Forensics: A Systematic Empirical Evaluation` annotations --- ...A Systematic Empirical Evaluation.xopp.xml | 622 +++++++++++++++++- 1 file changed, 608 insertions(+), 14 deletions(-) diff --git a/articles/On the Sensor Pattern Noise Estimation in Image Forensics: A Systematic Empirical Evaluation/On the Sensor Pattern Noise Estimation in Image Forensics: A Systematic Empirical Evaluation.xopp.xml b/articles/On the Sensor Pattern Noise Estimation in Image Forensics: A Systematic Empirical Evaluation/On the Sensor Pattern Noise Estimation in Image Forensics: A Systematic Empirical Evaluation.xopp.xml index 88ea0a3..eabb0b9 100644 --- a/articles/On the Sensor Pattern Noise Estimation in Image Forensics: A Systematic Empirical Evaluation/On the Sensor Pattern Noise Estimation in Image Forensics: A Systematic Empirical Evaluation.xopp.xml +++ b/articles/On the Sensor Pattern Noise Estimation in Image Forensics: A Systematic Empirical Evaluation/On the Sensor Pattern Noise Estimation in Image Forensics: A Systematic Empirical Evaluation.xopp.xml @@ -2,56 +2,650 @@ Xournal++ document - see https://github.com/xournalpp/xournalpp - - + + + 100.47116089 57.52011108 500.74999531 57.52011108 + 294.46197510 119.13845825 521.38992510 119.13845825 + 72.10986328 131.63259888 343.84353367 131.63259888 + 355.64074707 131.61053467 517.71260639 131.61053467 + 73.00711060 146.38546753 184.97795901 146.38546753 + 76.43453979 276.39059448 433.20058593 276.39059448 + 91.23587036 291.85012817 286.08548311 291.85012817 + 457.14361572 311.03555298 524.05283794 311.03555298 + 521.03094482 328.81024170 74.47716397 328.81024170 + 71.81842041 340.18911743 517.57241270 340.18911743 + 74.70880127 352.98400879 514.57411387 352.98400879 + 73.46508789 366.19616699 491.96009857 366.19616699 + 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533.73222287 56.72170608 563.79812739 58.44264420 + 316.87958413 68.88151564 565.30278932 68.88151564 + 317.37009413 80.44160465 557.73664478 80.44160465 + 356.09570785 90.55373150 505.95333704 90.55373150 + 351.11633575 103.58434249 563.42659263 103.58434249 + 314.64549552 116.24925841 341.66714394 116.24925841 + also unclear + 328.28897185 141.84011093 559.84018254 141.84011093 + 315.77985787 153.93415626 482.32183228 153.93415626 + 313.15973205 171.17605673 476.64320655 171.17605673 + 544.41864763 172.01302848 564.71180036 172.01302848 + 314.29748140 183.12601349 398.42170671 183.12601349 + 469.79221752 183.09278864 560.32242527 183.09278864 + 314.52586189 196.06118369 561.17116063 196.06118369 + 334.45532574 206.36481710 521.17087274 206.36481710 + 329.15834186 506.14777882 395.40958941 506.14777882 + 555.53534220 501.57274987 563.64922041 501.02238272 + 314.11772853 514.88675991 402.03462704 514.88675991 + 487.79532580 507.78277185 526.08268513 507.78277185 + 448.94411088 518.78017552 564.72922962 518.78017552 + 314.46167020 530.43905199 492.51114565 530.43905199 + 417.30930012 536.95872228 559.21031725 536.95872228 + 316.48128920 548.74354269 561.34302195 548.74354269 + 314.84012699 559.11451287 555.77334727 559.11451287 + Bayer filter impact? + 314.45445266 569.27679956 322.70932432 569.27679956 + 457.60555389 570.98348407 563.80447987 570.98348407 + 318.33026797 582.28045569 491.91832847 582.28045569 + 506.24217125 581.97788379 563.73955235 581.97788379 + 316.33605146 593.56996784 342.40645168 593.56996784 + 354.34362143 593.71484268 409.89959511 593.71484268 + 484.71835835 592.37705880 503.18156110 592.37705880 + 540.85520232 593.37602184 563.99765226 593.37602184 + 315.65877267 605.29531416 562.90154713 605.29531416 + as most green in Bayer filter? + 315.55607402 617.15372116 454.41995963 617.15372116 + 478.06008321 622.28994356 547.66391733 622.28994356 + 349.52944617 632.72956094 409.27205882 632.72956094 + 421.48192137 626.01475517 562.03099914 626.01475517 + 314.45876705 640.24067432 416.40492356 640.24067432 + 532.92438333 201.34304704 559.98497156 202.72266439 + 315.18394769 210.82007130 567.51215396 210.82007130 + 316.68467041 222.69853819 494.86645099 222.69853819 + 343.78154791 236.41033915 470.92262666 236.41033915 + =12² + so at least 12*64 = 768 so 768x768 resolution + 532.96752724 235.67128795 564.37211959 235.67128795 + 402.22530095 245.93556766 513.39320194 245.93556766 + note that doing so multiply dataset size by 144 but have +to pay attention to compare only same location sub-images + 407.58901677 271.55708462 563.15748653 271.55708462 + 314.80053136 282.60416427 464.67815255 282.60416427 + 526.15280501 283.88424821 565.87356748 283.88424821 + 333.97662991 293.65257453 533.64358611 293.65257453 + why such batch? + 348.85490923 309.43165440 471.10787664 309.43165440 + 531.96610460 311.38079157 564.46932907 311.38079157 + 312.71506749 323.31242225 566.30174446 323.31242225 + 314.44247720 334.49816724 400.23589253 334.49816724 + 398.22267479 350.85339981 559.42956341 350.85339981 + 315.83719492 363.10211809 561.04697617 363.10211809 + 313.98438561 375.98839857 402.32162396 375.98839857 + model seems to mean instance + sem to +introduce +scalar for measuring +0: distinct +classes +1: identical class + so do not measure between inter models even if easier? + 411.92381074 378.35945902 561.17891258 378.35945902 + 313.10924964 390.84357372 562.70011455 390.84357372 + 312.82171763 402.73133470 402.20455947 402.73133470 + 416.15159168 397.16562876 564.73565856 397.16562876 + 315.51119629 409.06861107 486.03767106 409.06861107 + 521.33112996 408.66130430 565.18470853 408.66130430 + 372.35664879 425.65165963 560.78783655 425.65165963 + 314.77343537 438.50046136 483.78978854 438.50046136 + 510.43015495 433.50318655 564.10091104 433.50318655 + 313.87596133 443.88756360 559.11271414 443.88756360 + and we compare the same subimage location, right? + figure to illustrate would be nice +should not remove unique cameras, otherwise +there is a same camera bias, no? As it was +described + 234.08770472 206.17588104 502.72826132 474.81643763 + 416.51404087 454.54100544 550.43477157 454.54100544 + 314.72021110 466.54507354 400.43169699 466.54507354 + 510.78167704 466.09077217 558.24367989 466.09077217 + 316.68535587 478.77661490 444.28418845 478.77661490 + 321.01482742 676.32746316 560.17952081 676.32746316 + 359.32246935 690.22508536 478.44007560 690.22508536 + 425.16471806 702.36228469 450.31572839 702.36228469 + 350.75828159 714.95010606 479.18200685 714.95010606 + 504.81741408 716.03352231 560.83576335 716.03352231 + 313.74701345 728.67616065 402.09293435 728.67616065 + if it was black, then would be multiplying by 0 +seems correct, as we speak about average signal, +hence there is no other noise +unsure why a white Gaussian + 413.64392228 728.32477968 547.50501184 728.32477968 + - + + 376.17403776 602.89536007 499.04176889 602.89536007 + 315.29496076 674.99792728 412.55942772 674.99792728 + 125.47796324 54.13670020 295.53544245 54.13670020 + 125.10105882 99.46996553 144.02233845 99.46996553 + 126.02077012 109.05480925 142.41319924 109.05480925 + 125.09337759 154.41339643 153.33576999 154.41339643 + 126.24771920 164.63683055 154.00387689 164.63683055 + 126.09802595 190.89199853 142.87048435 192.05379960 + 125.82355808 210.96309135 138.79590397 210.96309135 + 125.51812337 275.35745856 145.39004857 273.45082016 + 126.99318151 283.39400029 150.33093419 283.39400029 + 124.61085123 293.01015359 151.07736390 291.07496763 + 125.25492534 376.25770530 148.46593285 376.25770530 + 247.89276912 413.25421581 259.99757820 413.25421581 + 78.08519095 414.29531472 87.07026371 414.85106478 + 248.82596793 100.12359582 258.85251258 99.54262472 + 147.12673220 435.52500307 210.28154668 435.52500307 + 119.64470455 480.34860572 135.70432066 481.18596053 + 118.58340460 499.02596869 149.52604796 499.02596869 + 119.17038359 516.42885241 142.10220012 518.09767510 + 119.32197195 534.29301310 137.55317848 535.16452015 + 119.57055347 553.46491822 143.94960052 553.46491822 + 76.77207369 562.01277581 81.10161580 562.01277581 + 247.87395918 563.97288040 257.52345829 563.97288040 + 246.21182983 481.35319360 258.89987009 479.47766317 + 234.13736055 81.29403967 276.60685457 81.29403967 + 69.32682534 81.69505629 106.03165759 81.69505629 + 47.88684066 588.60170429 294.02457452 588.60170429 + 105.92707714 605.16376555 297.10612134 605.16376555 + 47.03899220 617.27688292 266.31473397 617.27688292 + 66.14166545 627.13246477 138.39288437 627.13246477 + 89.79985210 638.80392153 186.53215055 638.80392153 + 184.61191809 627.52668724 210.59158518 627.52668724 + 218.35326066 635.25993130 294.34680174 635.25993130 + 47.86383729 645.42959681 233.96796811 645.42959681 + 47.82577388 656.84458921 293.04157191 656.84458921 + 48.94575156 670.52838695 219.36149339 670.52838695 + 56.03586906 686.63773953 199.32924529 686.63773953 + 247.64682865 680.53767404 297.03768941 680.53767404 + 47.91330159 691.41887139 293.07755011 691.41887139 + 48.87609632 703.41298010 127.03449182 703.41298010 + 153.59025492 703.79042886 249.27876468 703.79042886 + 49.40954863 714.11817448 83.39023079 714.11817448 + 105.65317378 714.70460913 216.78085086 714.70460913 + 273.63200444 703.14724182 296.09920510 703.14724182 + 273.07577053 715.06488097 293.84315019 715.06488097 + 50.68206223 725.42800863 204.47025845 725.42800863 + 236.92516297 726.33963549 294.07434535 726.33963549 + 317.55093568 56.15783126 561.63729005 56.15783126 + 314.24933763 69.47278886 490.90072036 69.47278886 + what would be done for no common cameras? 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