Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones
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Add clipping

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Benjamin Loison 2024-07-04 03:58:30 +02:00
parent a17ec28cc3
commit 6f961a46cf
Signed by: Benjamin_Loison
SSH Key Fingerprint: SHA256:BtnEgYTlHdOg1u+RmYcDE0mnfz1rhv5dSbQ2gyxW8B8
2 changed files with 16 additions and 12 deletions
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7
algorithms/image_utils/image_utils.py
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@ -30,12 +30,15 @@ def getPrnuShownAsSuch(size, gaussianNoise = 0):
break
# Center vertically, especially in the case `HEIGHT` > `WIDTH`.
draw.text((0, HEIGHT // 2 - fontSize // 2), TEXT, 255, font = font)
imageNpArray = np.array(imagePil)
imageNpArray = np.array(imagePil, dtype = np.float64)
#print(imageNpArray.dtype)
#exit(1)
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]
#print(prnuShownAsSuch[y, x])
prnuShownAsSuch[y, x] = gaussianNoiseNpArray[y, x]
return prnuShownAsSuch

21
datasets/noise_free_test_images/estimate_prnu.py
View File

@ -74,14 +74,17 @@ for splitNXNIndex, splitNXN in enumerate(SPLIT_N_X_N_S):
axis[0].set_title('First image without noise')
axisImShow(axis[0], imageWithoutPrnuNpArrayTile)
axis[1].set_title('Actual Gaussian noised PRNU')
axisImShow(axis[1], prnuNpArray)
axis[1].set_title('First image Gaussian noise')
axisImShow(axis[1], imageNoise)
axis[2].set_title('F. i. with G. n.')
axisImShow(axis[2], imageWithoutPrnuNpArray + imageNoise)
axis[2].set_title('First image with Gaussian noise')
axisImShow(axis[2], np.clip(imageWithoutPrnuNpArray + imageNoise, 0, 255))
axis[3].set_title('F. i. with G. n. and PRNU')
axisImShow(axis[3], imageWithoutPrnuNpArray + prnuNpArray + imageNoise)
axis[3].set_title('Actual Gaussian noised PRNU')
axisImShow(axis[3], prnuNpArray)
axes[1][0].set_title('First image with Gaussian noise and PRNU')
axisImShow(axes[1][0], np.clip(imageWithoutPrnuNpArray + prnuNpArray + imageNoise, 0, 255))
isFirstImage = False
#assert all([isIn256Range(extreme) for extreme in [imageWithPrnuNpArray.max(), imageWithPrnuNpArray.min()]]), 'Adding the PRNU resulted in out of 256 bounds image'
@ -96,10 +99,8 @@ for splitNXNIndex, splitNXN in enumerate(SPLIT_N_X_N_S):
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')
axis[splitNXNIndex + 1].set_title(f'PRNU estimation\nwith {len(imagesPrnuEstimateNpArray)} images')#\n{title}
axisImShow(axis[splitNXNIndex + 1], cameraPrnuEstimateNpArray)
plt.tight_layout()
plt.show()