Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones
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Make PRNU compatible with 4x4 split

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Benjamin Loison
2024-03-29 11:23:48 +01:00
parent 8351e46437
commit 5fa61f7ff8
1 changed files with 7 additions and 19 deletions
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26
datasets/noise_free_test_images/estimate_prnu.py
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@@ -24,13 +24,14 @@ 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 = 0.25
NOISE_FACTOR = 0.1
IMAGE_SIZE_SHAPE = (469, 704)
IMAGE_SIZE_SHAPE_4x4 = [size // 4 for size in IMAGE_SIZE_SHAPE]
np.random.seed(0)
#prnuNpArray = 255 * randomGaussianImage(scale = PRNU_FACTOR, size = IMAGE_SIZE_SHAPE)
prnuPil = Image.open('prnu.png').convert('F')
prnuPil = Image.open('prnu_4x4_noise.png').convert('F')
prnuNpArray = np.array(prnuPil) * PRNU_FACTOR
def isIn256Range(x):
@@ -44,25 +45,13 @@ for imageName in os.listdir(datasetPath):
imageWithoutPrnuPil = Image.open(imagePath).convert('F')
imageWithoutPrnuNpArray = np.array(imageWithoutPrnuPil)
m = imageWithoutPrnuNpArray.shape[0] // 1
n = imageWithoutPrnuNpArray.shape[1] // 1
m = imageWithoutPrnuNpArray.shape[0] // 4
n = imageWithoutPrnuNpArray.shape[1] // 4
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:
imageNoise = randomGaussianImage(scale = 255 * NOISE_FACTOR, size = imageWithoutPrnuNpArrayTile.shape)
imageWithPrnuNpArray = imageWithoutPrnuNpArrayTile + prnuNpArray + imageNoise
#showImageWithMatplotlib(imageWithPrnuNpArray)
fig, axes = plt.subplots(1, 2)
fig.suptitle('Comparison of an image without and with Gaussian noise and PRNU')
axes[0].set_title('Image without Gaussian noise and PRNU')
axes[0].imshow(imageWithoutPrnuNpArray)
axes[1].set_title('Image with Gaussian noise and PRNU')
axes[1].imshow(imageWithPrnuNpArray)
plt.show()
break
#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)
@@ -70,9 +59,8 @@ for imageName in os.listdir(datasetPath):
imagePrnuEstimateNpArray = imageWithPrnuNpArray - denoise_tv_chambolle(imageWithPrnuNpArray, weight=0.2, channel_axis=-1)
imagesPrnuEstimateNpArray += [imagePrnuEstimateNpArray]
break
cameraPrnuEstimateNpArray = np.array(imagesPrnuEstimateNpArray).mean(axis = 0)
#rms = rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray, True)
#showImageWithMatplotlib(cameraPrnuEstimateNpArray, f'Camera PRNU estimate\nRMS with actual one: {rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray):.4f} (normalized RMS: {rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray, True):.4f})')
rms = rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray, True)
showImageWithMatplotlib(cameraPrnuEstimateNpArray, f'Camera PRNU estimate\nRMS with actual one: {rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray):.4f} (normalized RMS: {rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray, True):.4f})')