Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones
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#21: Make a RMS curve depending on the number of images considered for the mean

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Benjamin Loison 2024-03-26 01:59:22 +01:00
parent c2862eaf43
commit 2bc13c5949
Signed by: Benjamin_Loison
SSH Key Fingerprint: SHA256:BtnEgYTlHdOg1u+RmYcDE0mnfz1rhv5dSbQ2gyxW8B8
1 changed files with 7 additions and 5 deletions
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12
datasets/fake/generate_dataset.py
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@ -18,10 +18,12 @@ NUMBER_OF_IMAGES_PER_PHONE = 10_000
# Compared to images being 1.
PRNU_FACTOR = 0.1
IMAGE_SIZE_SHAPE = (IMAGE_SIZE, IMAGE_SIZE)
# Generate PRNUs and images 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))
return np.random.normal(loc = 0, scale = scale, size = IMAGE_SIZE_SHAPE)
imagesWithoutPrnu = [[randomImage(scale = 1) for _ in range(NUMBER_OF_IMAGES_PER_PHONE)] for phoneIndex in range(NUMBER_OF_PHONES)]
@ -47,11 +49,11 @@ plt.title('RMS between actual PRNU and the mean of the first $N$ images with PRN
plt.xlabel('$N$ first images with PRNU')
plt.ylabel('RMS')
rmss = []
mean = 0
mean = np.zeros(IMAGE_SIZE_SHAPE)
for imageIndex in range(NUMBER_OF_IMAGES_PER_PHONE):
rms = rmsDiffNumpy(imagesWithPrnu[0][imageIndex], prnus[0])
mean = ((mean * imageIndex) + rms) / (imageIndex + 1)
rmss += [mean]
mean = (mean * imageIndex + imagesWithPrnu[0][imageIndex]) / (imageIndex + 1)
rms = rmsDiffNumpy(mean, prnus[0])
rmss += [rms]
plt.plot(rmss)
plt.show()