Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones
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Add PRNU showing such

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Benjamin Loison 2024-03-28 22:24:16 +01:00
parent ba5a1b742b
commit 9a3cfd7ba1
Signed by: Benjamin_Loison
SSH Key Fingerprint: SHA256:BtnEgYTlHdOg1u+RmYcDE0mnfz1rhv5dSbQ2gyxW8B8
1 changed files with 6 additions and 6 deletions
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12
datasets/noise_free_test_images/estimate_prnu.py
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@ -12,20 +12,20 @@ from image_utils import showImageWithMatplotlib, randomGaussianImage
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 = 255 * 0.1
PRNU_FACTOR = 0.1
IMAGE_SIZE_SHAPE = (469, 704)
np.random.seed(0)
# Should first try with an image showing `PRNU`.
prnu = randomGaussianImage(scale = PRNU_FACTOR, size = IMAGE_SIZE_SHAPE)
#prnuNpArray = 255 * randomGaussianImage(scale = PRNU_FACTOR, size = IMAGE_SIZE_SHAPE)
prnuPil = Image.open('prnu.png').convert('F')
prnuNpArray = np.array(prnuPil) * PRNU_FACTOR
for imageName in os.listdir(datasetPath):
if imageName.endswith('.png'):
#print(imageName)
imagePath = f'{datasetPath}/{imageName}'
imagePil = Image.open(imagePath).convert('F')
imageNpArray = np.array(imagePil)
showImageWithMatplotlib(imageNpArray)
showImageWithMatplotlib(imageNpArray + prnu)
#showImageWithMatplotlib(imageNpArray)
showImageWithMatplotlib(imageNpArray + prnuNpArray)
break