Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones
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Benjamin Loison 2024-04-30 05:31:14 +02:00
parent b6425b426e
commit 3c403dbed3
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1 changed files with 41 additions and 17 deletions
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58
datasets/raise/attribute_source_camera.py
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@ -27,30 +27,42 @@ random.seed(0)
for camera in IMAGES_CAMERAS_FOLDER:
random.shuffle(imagesCamerasFileNames[camera])
minimumNumberOfImagesCameras = 10#min([len(imagesCamerasFileNames[camera]) for camera in IMAGES_CAMERAS_FOLDER])
minimumNumberOfImagesCameras = 4#min([len(imagesCamerasFileNames[camera]) for camera in IMAGES_CAMERAS_FOLDER])
for camera in IMAGES_CAMERAS_FOLDER:
IMAGES_CAMERAS_FOLDER[camera] = IMAGES_CAMERAS_FOLDER[camera][:minimumNumberOfImagesCameras]
imagesCamerasFileNames[camera] = imagesCamerasFileNames[camera][:minimumNumberOfImagesCameras]
numberOfCameras = len(IMAGES_CAMERAS_FOLDER)
camerasIterativeMean = {camera: iterativeMean() for camera in IMAGES_CAMERAS_FOLDER}
minColor = None
maxColor = None
# Assume that for each camera, its images have the same resolution.
# The following consider a given color channel resolution, assuming they all have the same resolution.
minimalColorChannelCameraResolution = None
for camera in IMAGES_CAMERAS_FOLDER:
imageFileName = imagesCamerasFileNames[camera][0]
imageFilePath = f'{IMAGES_CAMERAS_FOLDER[camera]}/{imageFileName}'
singleColorChannelImagesShape = getColorChannel(imageFilePath, Color.RED).shape
#print(singleColorChannelImagesShape)
if minimalColorChannelCameraResolution is None or singleColorChannelImagesShape < minimalColorChannelCameraResolution:
minimalColorChannelCameraResolution = singleColorChannelImagesShape
#print(minimalColorChannelCameraResolution)
#exit(1)
accuracy = []
numberOfTrainingImages = int(minimumNumberOfImagesCameras * TRAINING_PORTION)
numberOfTestingImages = minimumNumberOfImagesCameras - int(minimumNumberOfImagesCameras * TRAINING_PORTION)
cameraTestingImagesNoise = {}#{camera: [] for camera in IMAGES_CAMERAS_FOLDER}
cameraTestingImagesNoise = {}
returnSingleColorChannelImage = lambda singleColorChannelImage, _minColor, _maxColor: singleColorChannelImage
for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else []) + [False], 'Compute extremes'):
rescaleIfNeeded = returnSingleColorChannelImage if computeExtremes else rescaleRawImageForDenoiser
for cameraTrainingImageIndex in tqdm(range(minimumNumberOfImagesCameras if computeExtremes else numberOfTrainingImages), 'Camera training image index'):
for camera in tqdm(IMAGES_CAMERAS_FOLDER, 'Camera'):
for cameraIndex, camera in enumerate(tqdm(IMAGES_CAMERAS_FOLDER, 'Camera')):
imageFileName = imagesCamerasFileNames[camera][cameraTrainingImageIndex]
imageFilePath = f'{IMAGES_CAMERAS_FOLDER[camera]}/{imageFileName}'
singleColorChannelImages = {color: rescaleIfNeeded(getColorChannel(imageFilePath, color), minColor, maxColor) for color in Color}
singleColorChannelImages = {color: rescaleIfNeeded(getColorChannel(imageFilePath, color)[:minimalColorChannelCameraResolution[0],:minimalColorChannelCameraResolution[1]], minColor, maxColor) for color in Color}
multipleColorsImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages)
if computeExtremes:
@ -64,29 +76,41 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
cameraIterativeMean = camerasIterativeMean[camera]
cameraIterativeMean.add(imagePrnuEstimateNpArray)
if cameraIndex == numberOfCameras - 1:
for cameraTestingImageIndex in tqdm(range(numberOfTrainingImages), 'Camera testing image index'):
singleColorChannelImages = {color: rescaleIfNeeded(getColorChannel(imageFilePath, color), minColor, maxColor) for color in Color}
multipleColorsImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages)
singleColorChannelDenoisedImages = {color: denoise(singleColorChannelImages[color], DENOISER) for color in Color}
multipleColorsDenoisedImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelDenoisedImages)
rms = rmsDiffNumpy(subgroupIterativeMean.mean, subgroupsIterativeMean[1 - cameraIndex].mean)
accuracy += [rms]
numberOfTrainingImagesAccuracy = 0
# Loop over each camera testing image folder.
for actualCamera in IMAGES_CAMERAS_FOLDER:
for cameraTestingImageIndex in tqdm(range(numberOfTestingImages), 'Camera testing image index'):
cameraPredicted = None
minimalDistance = None
# Loop over each camera to compute closeness between the considered testing image noise and the estimated PRNUs of the various cameras.
for camera in IMAGES_CAMERAS_FOLDER:
distance = rmsDiffNumpy(cameraTestingImagesNoise[camera][cameraTestingImageIndex], camerasIterativeMean[camera].mean)
print(f'{cameraTestingImageIndex=} {camera=} {actualCamera=} {distance=}')
if minimalDistance is None or distance < minimalDistance:
minimalDistance = distance
cameraPredicted = camera
if cameraPredicted == actualCamera:
numberOfTrainingImagesAccuracy += 1
accuracy += [numberOfTrainingImagesAccuracy / (numberOfTestingImages * numberOfCameras)]
if computeExtremes:
print(f'{minColor=} {maxColor=}')
print('Extracting noise of testing images')
for camera in tqdm(IMAGES_CAMERAS_FOLDER, 'Camera'):
for cameraTestingImageIndex in tqdm(range(numberOfTestingImages), 'Camera testing image index'):
imageFilePath =
singleColorChannelImages = {color: rescaleIfNeeded(getColorChannel(imageFilePath, color), minColor, maxColor) for color in Color}
# Should make a function
imageFileName = imagesCamerasFileNames[camera][numberOfTrainingImages + cameraTestingImageIndex]
imageFilePath = f'{IMAGES_CAMERAS_FOLDER[camera]}/{imageFileName}'
# Should make a function
singleColorChannelImages = {color: rescaleIfNeeded(getColorChannel(imageFilePath, color)[:minimalColorChannelCameraResolution[0],:minimalColorChannelCameraResolution[1]], minColor, maxColor) for color in Color}
multipleColorsImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages)
singleColorChannelDenoisedImages = {color: denoise(singleColorChannelImages[color], DENOISER) for color in Color}
multipleColorsDenoisedImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelDenoisedImages)
imagePrnuEstimateNpArray = multipleColorsImage - multipleColorsDenoisedImage
cameraTestingImagesNoise[camera] = cameraTestingImagesNoise.get(camera, []) + [multipleColorsDenoisedImage]
for camera in range(IMAGES_CAMERAS_FOLDER):
for camera in IMAGES_CAMERAS_FOLDER:
plt.imsave(f'{setting}_estimated_prnu_subgroup_{escapeFilePath(camera)}.png', (camerasIterativeMean[camera].mean))
plt.title(f'Accuracy of camera source attribution thanks to a given number of images to estimate PRNUs with {DENOISER} denoiser')