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			prnu_writt
		
	
	| Author | SHA1 | Date | |
|---|---|---|---|
| ccb2b4bd86 | 
							
								
								
									
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							| @@ -1,3 +1,2 @@ | ||||
| *.pdf | ||||
| *.xopp | ||||
| __pycache__ | ||||
|   | ||||
| @@ -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() | ||||
|   | ||||
| @@ -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))) | ||||
| @@ -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 +0,0 @@ | ||||
| https://en.wikipedia.org/w/index.php?title=Darktable&oldid=1204679832 | ||||
| @@ -1,24 +0,0 @@ | ||||
| <?xml version="1.0" standalone="no"?> | ||||
| <xournal creator="Xournal++ 1.1.3" fileversion="4"> | ||||
|   <title>Xournal++ document - see https://github.com/xournalpp/xournalpp</title> | ||||
|   <page width="596.00000000" height="842.00000000"> | ||||
|     <background type="pdf" domain="absolute" filename="/home/benjamin/Desktop/bens_folder/school/ens/asp/aria/internship/work/articles/Darktable - Wikipedia/Darktable - Wikipedia.pdf" pageno="1"/> | ||||
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|   </page> | ||||
| </xournal> | ||||
| @@ -1 +0,0 @@ | ||||
| https://en.wikipedia.org/w/index.php?title=IrfanView&oldid=1214028358 | ||||
| @@ -1,26 +0,0 @@ | ||||
| <?xml version="1.0" standalone="no"?> | ||||
| <xournal creator="Xournal++ 1.1.3" fileversion="4"> | ||||
|   <title>Xournal++ document - see https://github.com/xournalpp/xournalpp</title> | ||||
|   <page width="596.00000000" height="842.00000000"> | ||||
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| </xournal> | ||||
| @@ -1 +0,0 @@ | ||||
| https://en.wikipedia.org/w/index.php?title=Law_of_large_numbers&oldid=1214684685 | ||||
| @@ -1,62 +0,0 @@ | ||||
| <?xml version="1.0" standalone="no"?> | ||||
| <xournal creator="Xournal++ 1.1.3" fileversion="4"> | ||||
|   <title>Xournal++ document - see https://github.com/xournalpp/xournalpp</title> | ||||
|   <page width="596.00000000" height="842.00000000"> | ||||
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| @@ -1 +0,0 @@ | ||||
| https://fr.wikipedia.org/w/index.php?title=Nikon_Electronic_File&oldid=209775164 | ||||
| @@ -1,40 +0,0 @@ | ||||
| <?xml version="1.0" standalone="no"?> | ||||
| <xournal creator="Xournal++ 1.1.3" fileversion="4"> | ||||
|   <title>Xournal++ document - see https://github.com/xournalpp/xournalpp</title> | ||||
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| @@ -1 +0,0 @@ | ||||
| https://en.wikipedia.org/w/index.php?title=Raster_graphics&oldid=1215085649 | ||||
| @@ -1 +0,0 @@ | ||||
| https://en.wikipedia.org/w/index.php?title=Shot_noise&oldid=1210973018 | ||||
| @@ -1,111 +0,0 @@ | ||||
| <?xml version="1.0" standalone="no"?> | ||||
| <xournal creator="Xournal++ 1.1.3" fileversion="4"> | ||||
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| @@ -1 +0,0 @@ | ||||
| https://en.wikipedia.org/w/index.php?title=TIFF&oldid=1216190460 | ||||
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| @@ -1,5 +1,3 @@ | ||||
| # Notes: https://gitea.lemnoslife.com/Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones/issues/21 | ||||
|  | ||||
| import numpy as np | ||||
| from matplotlib import pyplot as plt | ||||
| from PIL import Image | ||||
| @@ -12,10 +10,6 @@ from rms_diff import rmsDiffPil, rmsDiffNumpy | ||||
| sys.path.insert(0, '../../algorithms/context_adaptive_interpolator/') | ||||
|  | ||||
| from context_adaptive_interpolator import contextAdaptiveInterpolator | ||||
|  | ||||
| sys.path.insert(0, '../../algorithms/image_utils/') | ||||
|  | ||||
| from image_utils import showImageWithMatplotlib, randomGaussianImage, toPilImage | ||||
| from tqdm import tqdm | ||||
|  | ||||
| IMAGE_SIZE = 64 | ||||
| @@ -24,38 +18,30 @@ NUMBER_OF_IMAGES_PER_PHONE = 10_000 | ||||
| # Compared to images being 1. | ||||
| PRNU_FACTOR = 0.1 | ||||
|  | ||||
| IMAGE_SIZE_SHAPE = (IMAGE_SIZE, IMAGE_SIZE) | ||||
|  | ||||
| np.random.seed(0) | ||||
|  | ||||
| # Generate PRNUs and images of phones. | ||||
| imagesWithoutPrnu = [[randomGaussianImage(scale = 1, size = IMAGE_SIZE_SHAPE) for _ in range(NUMBER_OF_IMAGES_PER_PHONE)] for phoneIndex in range(NUMBER_OF_PHONES)] | ||||
| # Is such `np.maximum` probabilistically correct with our theoretical method? See #19. | ||||
| def randomImage(scale): | ||||
|     return np.random.normal(loc = 0, scale = scale, size = (IMAGE_SIZE, IMAGE_SIZE)) | ||||
|  | ||||
| prnus = [randomGaussianImage(scale = PRNU_FACTOR, size = IMAGE_SIZE_SHAPE) for _ in range(NUMBER_OF_PHONES)] | ||||
| imagesWithoutPrnu = [[randomImage(scale = 1) for _ in range(NUMBER_OF_IMAGES_PER_PHONE)] for phoneIndex in range(NUMBER_OF_PHONES)] | ||||
|  | ||||
| prnus = [np.array(Image.open('prnu.png').convert('F')) * PRNU_FACTOR / 255] | ||||
|  | ||||
| imagesWithPrnu = [[imageWithoutPrnu + prnus[phoneIndex] for imageWithoutPrnu in imagesWithoutPrnu[phoneIndex]] for phoneIndex in range(NUMBER_OF_PHONES)] | ||||
|  | ||||
| allImages = np.max([np.max(imagesWithoutPrnu) + np.max(prnus) + np.max(imagesWithPrnu)]) | ||||
|  | ||||
| def toPilImage(npArray): | ||||
|     return Image.fromarray(npArray) | ||||
|  | ||||
| def showImageWithPil(npArray): | ||||
|     npArray -= npArray.min() | ||||
|     npArray = (npArray / npArray.max()) * 255 | ||||
|     Image.fromarray(npArray).show() | ||||
|  | ||||
| plt.title('RMS between actual PRNU and the mean of the first $N$ images with PRNU (i.e. estimated PRNU)') | ||||
| plt.xlabel('$N$ first images with PRNU') | ||||
| plt.ylabel('RMS') | ||||
| plt.xscale('log') | ||||
| rmss = [] | ||||
| mean = np.zeros(IMAGE_SIZE_SHAPE) | ||||
| for imageIndex in range(NUMBER_OF_IMAGES_PER_PHONE): | ||||
|     mean = (mean * imageIndex + imagesWithPrnu[0][imageIndex]) / (imageIndex + 1) | ||||
|     rms = rmsDiffNumpy(mean, prnus[0]) | ||||
|     rmss += [rms] | ||||
| plt.plot(rmss) | ||||
| plt.show() | ||||
|  | ||||
| ## | ||||
| def showImageWithMatplotlib(npArray): | ||||
|     plt.imshow(npArray) | ||||
|     plt.show() | ||||
|  | ||||
| NUMBER_OF_ROWS = 5 | ||||
| NUMBER_OF_COLUMNS = 3 | ||||
| @@ -89,6 +75,23 @@ plt.show() | ||||
|  | ||||
| ## | ||||
|  | ||||
| def toFileName(title): | ||||
|     return title.lower().replace(' ', '_').replace(',', '_') | ||||
|  | ||||
| for title, image in zip(['Actual PRNU', 'First image without PRNU'], [prnus[0], imagesWithoutPrnu[0][0]]): | ||||
|     plt.title(title) | ||||
|     plt.imshow(image) | ||||
|     plt.savefig(title.lower().replace(' ', '_') + '.svg') | ||||
|  | ||||
| for numberOfImages in [10 ** power for power in range(NUMBER_OF_ROWS)]: | ||||
|     title = 'First image with PRNU' if numberOfImages == 1 else f'Mean of first {numberOfImages:,} images with PRNU' | ||||
|     image = np.array(imagesWithPrnu[0][:numberOfImages]).mean(axis = 0) | ||||
|     plt.title(f'{title}\ni.e. estimated PRNU\nRMS with actual PRNU = {round(rmsDiffNumpy(image, prnus[0]), 4)}') | ||||
|     plt.imshow(image) | ||||
|     plt.savefig(f'{toFileName(title)}.svg') | ||||
|  | ||||
| ## | ||||
|  | ||||
| # Compute CAI of phone images. | ||||
| caiImages = [[contextAdaptiveInterpolator(image.load(), image) for image in imagesWithPrnuPil[phoneIndex]] for phoneIndex in tqdm(range(NUMBER_OF_PHONES))] | ||||
| #caiImages[0][0].show() | ||||
|   | ||||
| @@ -1,100 +0,0 @@ | ||||
| # Notes: https://gitea.lemnoslife.com/Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones/issues/25 | ||||
|  | ||||
| import os | ||||
| from PIL import Image | ||||
| import numpy as np | ||||
| import matplotlib.pyplot as plt | ||||
| import sys | ||||
|  | ||||
| sys.path.insert(0, '../../algorithms/image_utils/') | ||||
|  | ||||
| from image_utils import showImageWithMatplotlib, randomGaussianImage, toPilImage, getPrnuShownAsSuch | ||||
|  | ||||
| sys.path.insert(0, '../../algorithms/context_adaptive_interpolator/') | ||||
|  | ||||
| from context_adaptive_interpolator import contextAdaptiveInterpolator | ||||
|  | ||||
| sys.path.insert(0, '../../algorithms/distance/') | ||||
|  | ||||
| from rms_diff import rmsDiffNumpy | ||||
|  | ||||
| from skimage.restoration import denoise_tv_chambolle | ||||
|  | ||||
| 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. | ||||
| # 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 | ||||
| NOISE_FACTOR = 0.1 | ||||
|  | ||||
| np.random.seed(0) | ||||
|  | ||||
| SPLIT_N_X_N_S = [1, 2, 4] | ||||
|  | ||||
| # len(SPLIT_N_X_N_S) | ||||
| fig, axes = plt.subplots(2, 4) | ||||
| fig.suptitle('PRNU estimation with different number of images having Gaussian noise and Gaussian noised PRNU') | ||||
|  | ||||
| for splitNXNIndex, splitNXN in enumerate(SPLIT_N_X_N_S): | ||||
|     IMAGE_SIZE_SHAPE = [dimension // splitNXN for dimension in (704, 469)] | ||||
|  | ||||
|     #prnuNpArray = 255 * randomGaussianImage(scale = PRNU_FACTOR, size = IMAGE_SIZE_SHAPE) | ||||
|     prnuNpArray = getPrnuShownAsSuch(IMAGE_SIZE_SHAPE, 255) * PRNU_FACTOR | ||||
|  | ||||
|     def isIn256Range(x): | ||||
|         return 0 <= x and x <= 255 | ||||
|  | ||||
|     imagesPrnuEstimateNpArray = [] | ||||
|     isFirstImage = True | ||||
|  | ||||
|     for imageName in os.listdir(datasetPath): | ||||
|         if imageName.endswith('.png'): | ||||
|             imagePath = f'{datasetPath}/{imageName}' | ||||
|             imageWithoutPrnuPil = Image.open(imagePath).convert('F') | ||||
|             imageWithoutPrnuNpArray = np.array(imageWithoutPrnuPil) | ||||
|  | ||||
|             m = IMAGE_SIZE_SHAPE[1] | ||||
|             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)] | ||||
|             for imageWithoutPrnuNpArrayTile in imageWithoutPrnuNpArrayTiles: | ||||
|                 #print(imageWithoutPrnuNpArrayTile.shape, tuple(IMAGE_SIZE_SHAPE[::-1])) | ||||
|                 #if imageWithoutPrnuNpArrayTile.shape != tuple(IMAGE_SIZE_SHAPE[::-1]): | ||||
|                 #    continue | ||||
|                 imageNoise = randomGaussianImage(scale = 255 * NOISE_FACTOR, size = imageWithoutPrnuNpArrayTile.shape) | ||||
|                 imageWithPrnuNpArray = imageWithoutPrnuNpArrayTile + prnuNpArray + imageNoise | ||||
|  | ||||
|                 if splitNXNIndex == 0 and isFirstImage: | ||||
|                     axis = axes[0] | ||||
|  | ||||
|                     axis[0].set_title('First image without noise') | ||||
|                     axis[0].imshow(imageWithoutPrnuNpArrayTile) | ||||
|  | ||||
|                     axis[1].set_title('Actual Gaussian noised PRNU') | ||||
|                     axis[1].imshow(prnuNpArray) | ||||
|  | ||||
|                     axis[2].set_title('F. i. with G. n.') | ||||
|                     axis[2].imshow(imageWithoutPrnuNpArray + imageNoise) | ||||
|  | ||||
|                     axis[3].set_title('F. i. with G. n. and PRNU') | ||||
|                     axis[3].imshow(imageWithoutPrnuNpArray + prnuNpArray + imageNoise) | ||||
|                 isFirstImage = False | ||||
|  | ||||
|                 #assert all([isIn256Range(extreme) for extreme in [imageWithPrnuNpArray.max(), imageWithPrnuNpArray.min()]]), 'Adding the PRNU resulted in out of 256 bounds image' | ||||
|                 imageWithPrnuPil = toPilImage(imageWithPrnuNpArray) | ||||
|                 #imagePrnuEstimatePil = contextAdaptiveInterpolator(imageWithPrnuPil.load(), imageWithPrnuPil) | ||||
|                 #imagePrnuEstimateNpArray = np.array(imagePrnuEstimatePil) | ||||
|                 imagePrnuEstimateNpArray = imageWithPrnuNpArray - denoise_tv_chambolle(imageWithPrnuNpArray, weight=0.2, channel_axis=-1) | ||||
|  | ||||
|                 imagesPrnuEstimateNpArray += [imagePrnuEstimateNpArray] | ||||
|  | ||||
|     cameraPrnuEstimateNpArray = np.array(imagesPrnuEstimateNpArray).mean(axis = 0) | ||||
|     rms = rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray, True) | ||||
|     title = f'RMS with actual PRNU: {rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray):.4f}\n(normalized RMS: {rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray, True):.4f})' | ||||
|     axis = axes[1] | ||||
|     axis[splitNXNIndex].set_title(f'Number of images: {len(imagesPrnuEstimateNpArray)}\n{title}') | ||||
|     axis[splitNXNIndex].imshow(cameraPrnuEstimateNpArray) | ||||
|  | ||||
| axes[1][3].axis('off') | ||||
|  | ||||
| plt.tight_layout() | ||||
| plt.show() | ||||
| @@ -1 +0,0 @@ | ||||
| https://web.archive.org/web/20220121204219/https://mcolom.perso.math.cnrs.fr/download/no_noise_images/no_noise_images.zip | ||||
| @@ -1 +0,0 @@ | ||||
| https://web.archive.org/web/20221230200626/https://mcolom.perso.math.cnrs.fr/pages/no_noise_images/ | ||||
| @@ -1,39 +0,0 @@ | ||||
| <?xml version="1.0" standalone="no"?> | ||||
| <xournal creator="Xournal++ 1.1.3" fileversion="4"> | ||||
|   <title>Xournal++ document - see https://github.com/xournalpp/xournalpp</title> | ||||
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|       <text font="Sans" size="11.00000000" x="48.63067627" y="32.63059998" color="#00c0ffff" ts="0" fn="">Almost identical content as `no_noise_images.zip/readme.txt`.</text> | ||||
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|   </page> | ||||
|   <page width="596.00000000" height="842.00000000"> | ||||
|     <background type="pdf" pageno="2"/> | ||||
|     <layer/> | ||||
|   </page> | ||||
|   <page width="596.00000000" height="842.00000000"> | ||||
|     <background type="pdf" pageno="3"/> | ||||
|     <layer/> | ||||
|   </page> | ||||
| </xournal> | ||||
| @@ -1,52 +0,0 @@ | ||||
| from PIL import Image | ||||
| import numpy as np | ||||
| import matplotlib.pyplot as plt | ||||
| from tqdm import tqdm | ||||
| from skimage.restoration import denoise_tv_chambolle | ||||
| import sys | ||||
|  | ||||
| sys.path.insert(0, '../../algorithms/image_utils/') | ||||
|  | ||||
| from image_utils import showImageWithMatplotlib, toPilImage | ||||
|  | ||||
| sys.path.insert(0, '../../algorithms/distance/') | ||||
|  | ||||
| from rms_diff import rmsDiffNumpy | ||||
|  | ||||
| NUMBER_OF_SUBGROUPS = 1 | ||||
|  | ||||
| IMAGES_FOLDER = 'flat-field/TIF' | ||||
| imagesFileNames = os.listdir(IMAGES_FOLDER) | ||||
| numberOfImagesPerSubgroup = len(imagesFileNames) // NUMBER_OF_SUBGROUPS | ||||
|  | ||||
| # 1 | ||||
| numberOfImagesThresholds = range(numberOfImagesPerSubgroup, numberOfImagesPerSubgroup + 1) | ||||
|  | ||||
| # Assume random image order to not introduce a bias. | ||||
| subgroupsPrnuEstimatesNpArray = [] | ||||
| for subgroupIndex in range(NUMBER_OF_SUBGROUPS): | ||||
|     imagesPrnuEstimateNpArray = [] | ||||
|     for imageFileName in tqdm(imagesFileNames[numberOfImagesPerSubgroup * subgroupIndex : numberOfImagesPerSubgroup * (subgroupIndex + 1)]): | ||||
|         imagePath = f'{IMAGES_FOLDER}/{imageFileName}' | ||||
|         imagePil = Image.open(imagePath).convert('F') | ||||
|         imageNpArray = np.array(imagePil) | ||||
|         imagePrnuEstimateNpArray = imageNpArray - denoise_tv_chambolle(imageNpArray, weight=0.2, channel_axis=-1) | ||||
|         imagesPrnuEstimateNpArray += [imagePrnuEstimateNpArray] | ||||
|  | ||||
|     subgroupPrnuEstimateNpArray = [] | ||||
|     # Not efficient mean computation. | ||||
|     for numberOfImagesIndex, numberOfImages in enumerate(numberOfImagesThresholds): | ||||
|         subgroupPrnuEstimateNpArray += [np.array(imagesPrnuEstimateNpArray[:numberOfImages]).mean(axis = 0)] | ||||
|         plt.imsave('prnu.png', subgroupPrnuEstimateNpArray[-1]) | ||||
|     subgroupsPrnuEstimatesNpArray += [subgroupPrnuEstimateNpArray] | ||||
|  | ||||
| rmss = [] | ||||
| for numberOfImagesIndex, numberOfImages in enumerate(numberOfImagesThresholds): | ||||
|     rms = rmsDiffNumpy(subgroupsPrnuEstimatesNpArray[0][numberOfImagesIndex], subgroupsPrnuEstimatesNpArray[1][numberOfImagesIndex]) | ||||
|     rmss += [rms] | ||||
|  | ||||
| plt.title('RMS between both subgroups estimated PRNUs for a given number of images among them') | ||||
| plt.xlabel('Number of images of each subgroup') | ||||
| plt.ylabel('RMS between both subgroups estimated PRNUs') | ||||
| plt.plot(rmss) | ||||
| plt.show() | ||||
| @@ -1 +0,0 @@ | ||||
| https://web.archive.org/web/20221206120232/http://loki.disi.unitn.it/RAISE/download.html | ||||
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