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Given device split into two groups and compare both estimated PRNUs and how they evolved when consider more and more images to estimate them within each group #31

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opened 2024-04-02 14:12:03 +02:00 by Benjamin_Loison · 3 comments
Benjamin_Loison commented 2024-04-02 14:12:03 +02:00
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Consider uniform images (example sky) with RAW images.

Just verifying the data loading as it is a different file type:

image

We notice the bottom-right shadow as in GIMP.

Figure_1

Figure_2

Figure_3

2m16s to execute above algorithm.

The only interesting image examples to render are:

  • an actual image
  • a noise extraction of such actual image
  • the mean of such noise for 5 images and 50 images
  • the difference (same color scale as will be quite identical because of the expected randomness of the PRNU?) and RMS for such means (1, 5 and 50) between both subgroups?
numberOfImages=1 round(rms, 4)=0.3788
numberOfImages=5 round(rms, 4)=0.1715
numberOfImages=50 round(rms, 4)=0.0541

so the test gives the expected wanted results.

Consider uniform images (example sky) with RAW images. Just verifying the data loading as it is a different file type: ![image](/attachments/fad539a5-1bd8-494d-a9ef-80fa546872cf) We notice the bottom-right shadow as in GIMP. ![Figure_1](/attachments/0d041c5b-64af-40b7-a753-c76f52c5f91b) ![Figure_2](/attachments/31234597-5e08-479e-ab53-1e70a91fd146) ![Figure_3](/attachments/a3e1100d-646a-481b-ad60-4afa6577db16) 2m16s to execute above algorithm. The only interesting image examples to render are: - an actual image - a noise extraction of such actual image - the mean of such noise for 5 images and 50 images - the difference (same color scale as will be quite identical because of the expected randomness of the PRNU?) and RMS for such means (1, 5 and 50) between both subgroups? ``` numberOfImages=1 round(rms, 4)=0.3788 numberOfImages=5 round(rms, 4)=0.1715 numberOfImages=50 round(rms, 4)=0.0541 ``` so the test gives the expected wanted results.
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Benjamin_Loison commented 2024-04-02 23:58:57 +02:00
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Figure_1

![Figure_1](/attachments/d32cbcbf-6226-4ef5-8cad-038e37a8ba4a)
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Benjamin_Loison commented 2024-04-03 15:13:51 +02:00
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Should print a 2D figure with the evolving PRNU (with all images for instance) with for each x value a representation of the distribution of PRNU pixel values to see if the PRNU does not converge to 0 globally. Pay attention to compare with the images scale.

Should print a 2D figure with the evolving PRNU (with all images for instance) with for each x value a representation of the distribution of PRNU pixel values to see if the PRNU does not converge to 0 globally. Pay attention to compare with the images scale.
Benjamin_Loison commented 2024-04-06 00:22:53 +02:00
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Methods enumerated in #25 (comment).

tv_chambolle: 3:35:52, 129.53s/it
wavelet: 06:58, 4.19s/it

np_array

np_array_0

We notice the same tiles as previously.

bilateral: 51:29:12, 1853.52s/it is insanely slow but insanely more precise it seems:

np_array

np_array_0

circles are now very clear as well as we can notice a whole image circle maybe being somehow the lens and we notice corners having issue maybe due to an optic issue. More precise description in minutes.

image

Brightness and contrast is not as good as in GIMP, see Benjamin_Loison/gimp/issues/24.

Squares are about 295x295 pixels.

Methods enumerated in https://gitea.lemnoslife.com/Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones/issues/25#issuecomment-1476. `tv_chambolle`: 3:35:52, 129.53s/it `wavelet`: 06:58, 4.19s/it ![np_array](/attachments/49044015-8676-4507-8b9a-d3c3010c2abf) ![np_array_0](/attachments/7cbb3092-9f44-4027-bc2f-c1181fd42613) We notice the same tiles as previously. `bilateral`: 51:29:12, 1853.52s/it is insanely slow but insanely more precise it seems: ![np_array](/attachments/21ac3f70-75fc-452c-b79f-01014cae2b42) ![np_array_0](/attachments/fd963d85-ff00-4749-8c5a-2d92fb632fef) circles are now very clear as well as we can notice a whole image circle maybe being somehow the lens and we notice corners having issue maybe due to an optic issue. More precise description in minutes. ![image](/attachments/b613b6dd-fdb3-4284-96c2-30a64230adfb) Brightness and contrast is not as good as in GIMP, see [Benjamin_Loison/gimp/issues/24](https://gitlab.gnome.org/Benjamin_Loison/gimp/-/issues/24). Squares are about 295x295 pixels.
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Reference: Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones#31
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