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DeepFakeDetectionSoftware/DeepFakeDetection/LispScripts/feature_extraction.lisp

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+(in-package :deep-fake-feature-extraction)
+
+(defun load-image (path)
+ "Load an image from the given path."
+ (opticl:load-image path))
+
+(defun extract-perceptual-hash (image)
+ "Extract a perceptual hash from an image."
+ (let* ((width (opticl:image-width image))
+         (height (opticl:image-height image))
+         (pixels (mapcar #'list (mapcar #'array-element (opticl:image-pixels image)))))
+    (map 'vector (lambda (i) (hash (subseq pixels i 0 width))) (list-seq (iota height))))))
+
+(defun extract-color-histogram (image)
+ "Extract the color histogram from an image."
+ ;; Use Opticl functions to analyze the image's color distribution
+ (let* ((colors (opticl:color-histogram image))
+         (counts (mapcar #'length colors)))
+    (list (mapcar (lambda (c) (list (car c) (cdr c))) colors) counts)))
+
+(defun extract-texture-features (image)
+ "Extract texture features from an image."
+ ;; Placeholder for texture feature extraction
+ ;; This could involve edge detection, texture analysis, etc.
+ (let* ((width (opticl:image-width image))
+         (height (opticl:image-height image))
+         (pixels (mapcar #'list (mapcar #'array-element (opticl:image-pixels image)))))
+    (list (edgedetect pixels)
+          (haralick-texture-features pixels)
+          (wavelet-transform (opticl:image-pixels image)))))
+
+(defun extract-features (image-path)
+ "Extract various features from an image."
+ (let* ((image (load-image image-path))
+        (perceptual-hash (extract-perceptual-hash image))
+        (color-histogram (extract-color-histogram image))
+        (texture-features (extract-texture-features image)))
+    (list perceptual-hash color-histogram texture-features)))
+
+(defun main ()
+ (let ((sample-image-path "path/to/sample-image.jpg"))
+    (format t "Extracted Features: ~A~%" (extract-features sample-image-path))))
+
+(main)

+

DeepFakeDetectionSoftware/DeepFakeDetection/LispScripts/gan.lisp

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+(in-package :deep-fake-gan)
+
+(use-package :pyui)
+
+(defun create-generator ()
+ (py:call-function 'tensorflow.keras.Sequential (list :name "flatten" :input_shape '(28 28 1) :output_shape '(256,) :activation 'tanh)
+                 (py:call-function 'tensorflow.keras.layers.Conv2D (list :name "conv1" :kernel_size 3 :channels 32 :activation 'relu) (py:call-function 'tensorflow.keras.layers.Conv2D (list :name "conv2" :kernel_size 3 :channels 64 :activation 'relu) (py:call-function 'tensorflow.keras.layers.Flatten))
+                 (py:call-function 'tensorflow.keras.layers.Dense (list :units 10 :activation 'softmax))
+                 :build)
+)
+
+(defun create-discriminator ()
+ (py:call-function 'tensorflow.keras.Sequential (list :name "flatten" :input_shape '(28 28 1) :output_shape '(10,) :activation 'relu)
+                 (py:call-function 'tensorflow.keras.layers.Conv2D (list :kernel_size 3 :channels 32 :activation 'relu) (py:call-function 'tensorflow.keras.layers.Conv2D (list :kernel_size 3 :channels 64 :activation 'relu) (py:call-function 'tensorflow.keras.layers.Flatten))
+                 (py:call-function 'tensorflow.keras.layers.Dense (list :units 1 :activation 'sigmoid))
+                 :build)
+)
+
+(defun train-generator (generator discriminator training-data)
+ (py:call-function 'tensorflow.keras.models.train_on_batch generator training-data :model generator :targets discriminator)
+ (py:call-function 'tensorflow.keras.metrics.categorical_crossentropy (list :name "ce" :targets discriminator) (py:call-function 'tensorflow.keras.metrics.sparse_categorical_accuracy (list :name "accuracy" :targets discriminator))
+                   :generator generator)
+)
+
+(defun train-discriminator (discriminator generator training-data)
+ (py:call-function 'tensorflow.keras.models.train_on_batch generator training-data :model discriminator :targets generator)
+ (py:call-function 'tensorflow.keras.metrics.categorical_crossentropy (list :name "ce" :targets generator) (py:call-function 'tensorflow.keras.metrics.sparse_categorical_accuracy (list :name "accuracy" :targets generator))
+                   :discriminator discriminator)
+)
+
+(defun main ()
+ (setf generator (create-generator))
+ (setf discriminator (create-discriminator))
+ (save-model generator "path/to/save/generator-model")
+ (save-model discriminator "path/to/save/discriminator-model")
+ (train-generator generator discriminator (path "path/to/training-data")))
+ (train-discriminator discriminator generator (path "path/to/training-data")))
+)
+
+(main)

DeepFakeDetectionSoftware/DeepFakeDetection/LispScripts/image_restoration.lisp

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+(defun load-corrupted-image (path)
+ "Load a corrupted image from the given path using CL-OPENCV or another image processing library."
+ (let ((image (open-image path))
+        (height (image-height image))
+        (width (image-width image)))
+    (if (> height 0)
+        (values image height width)
+        (error "Failed to load the image ~A" path))))
+(defun restore-image-with-gan (corrupted-image-path gan-model)
+ "Restore a corrupted image using a pre-trained GAN model."
+ (let ((image (open-image corrupted-image-path))
+        (height (image-height image))
+        (width (image-width image)))
+    (if (> height 0)
+        (let ((output (run-gan gan-model image)))
+            (save-image output (strcat corrupted-image-path "-restored.jpg")))
+        (error "Failed to load the corrupted image ~A" corrupted-image-path))))
+
+Finally, you can use the functions defined above to restore a corrupted image using GANs:
+
+(defun restore-image (image-path)
+ "Main function to restore the image using various techniques, including GANs."
+ (let ((image (load-corrupted-image image-path)))
+   (restore-image-with-gan image-path "path/to/pre-trained/gan/model")))
+(restore-image "path/to/corrupted-image.jpg")

DeepFakeDetectionSoftware/DeepFakeDetection/LispScripts/inpainting.lisp

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+(in-package :deep-fake-inpainting)
+
+(defpackage :cl-pac
+ (:use :cl)
+ (:export :load-image :detect-corrupted-areas :save-image))
+
+(defpackage :tensorflow
+ (:use :cl)
+ (:export :load-tensorflow :initialize-tensorflow-session))
+
+(defun load-tensorflow ()
+ "Load the TensorFlow library."
+ (ql:quickload :tensorflow))
+
+(defun initialize-tensorflow-session ()
+ "Initializes a TensorFlow session."
+ (tf:with-session ()
+    (tf:reset-default-graph 21)
+    (tf:initialize-keras-backend)
+    (tf:enable-all-gather)))
+
+(defun load-image (path)
+ "Load an image from the given path."
+ (load-image path))
+
+(defun detect-corrupted-areas (image)
+ "Detect corrupted or missing areas in the image."
+ (detect-corrupted-areas image))
+
+(defun inpaint-image (image corrupted-areas)
+ "Apply inpainting techniques to restore corrupted areas of the image using a GAN."
+ (inpaint-image image corrupted-areas))
+
+(defun save-image (image path)
+ "Save the processed image to the given path."
+ (save-image image path))
+
+(defun restore-image (image-path)
+ "Main function to restore the image using GAN-based inpainting."
+ (let ((image (load-image image-path)))
+   (setf image (detect-corrupted-areas image))
+   (setf image (inpaint-image image (detect-corrupted-areas image)))
+   (save-image image (truename image-path))))
+
+(load-tensorflow)
+(initialize-tensorflow-session)
+(in-package :tensorflow)
+(defun load-tensorflow-image (path)
+ "Load an image from the given path using TensorFlow."
+ (load-image path))
+
+(defun gan-inpainting (image corrupted-areas)
+ "Apply GAN-based inpainting to restore corrupted areas of the image."
+ (let ((gan (make-instance 'tensorflow:gan))
+        (session (tensorflow:get-session gan)))
+    (dolist (area corrupted-areas)
+      (let ((patch (make-instance 'tensorflow:patch :x (truename (first area)) :y (truename (second area)))
+                 (tf:gather (tf:constant (make-array (list 3 3) :element '(tf:int32)) session) (list 0 0 0))))
+        (dolist (i (range 3))
+          (dolist (j (range 3))
+            (tf:assign (slot-value patch '(truename i) session) (slot-value patch (truename j) session) :persistent-true))
+          (tf:assign (slot-value patch '(truename i) session) (slot-value gan '(truename i) session) :persistent-true))))
+    (gan-forward-propagation gan session)
+    (gan-backward-propagation gan session)
+    (gan-optimize gan session)
+    (dolist (area corrupted-areas)
+      (setf (slot-value image (first area) (second area)) (slot-value patch '(truename (first area)) (truename (second area)) session))))
+    image)
+ )
+
+(defun gan-forward-propagation (gan session)
+ "Perform forward propagation through the GAN."
+ (dolist (layer (tensorflow:layers gan))
+   (when (typep layer 'tensorflow:dense)
+     (dolist (i (range (slot-value layer 'units) session))
+       (tf:assign (slot-value layer 'output i session) (tf:add (slot-value layer 'kernel i session) (slot-value layer 'bias i session) session))))
+   (when (typep layer 'tensorflow:convolutional)
+     (dolist (filter (slot-value layer 'filters))
+       (dolist (i (range (slot-value layer 'kernel-size) (slot-value layer 'kernel-size) session))
+         (dolist (j (range (slot-value layer 'kernel-size) (slot-value layer 'kernel-size) session))
+           (tf:assign (slot-value layer 'output i j session) (tf:add (slot-value layer 'kernel i j session) (slot-value layer 'bias i j session) session))))
+       (dolist (i (range (slot-value layer 'kernel-size) (slot-value layer 'kernel-size) session))
+         (dolist (j (range (slot-value layer 'kernel-size) (slot-value layer 'kernel-size) session))
+           (tf:assign (slot-value layer 'output i j session) (tf:add (slot-value layer 'kernel i j session) (slot-value layer 'bias i j session) session)))))
+       (dolist (i (range (slot-value layer 'kernel-size) (slot-value layer 'kernel-size) session))
+         (dolist (j (range (slot-value layer 'kernel-size) (slot-value layer 'kernel-size) session))
+           (tf:assign (slot-value layer 'output i j session) (tf:add (slot-value layer 'kernel i j session) (slot-value layer 'bias i j session) session)))))
+       (dolist (i (range (slot-value layer 'kernel-size) (slot-value layer 'kernel-size) session))
+         (dolist (j (range (slot-value layer 'kernel-size) (slot-value layer 'kernel-size) session))
+           (tf:assign (slot-value layer 'output i j session) (tf:add (slot-value layer 'kernel i j session) (slot-value layer 'bias i j session) session)))))))
+ (dolist (layer (tensorflow:layers gan))
+   (when (typep layer 'tensorflow:upsampling)
+     (dolist (i (range (slot-value layer 'size) session))
+       (dolist (j (range (slot-value layer 'size) session))
+         (tf:assign (slot-value layer 'output i j session) (tf:add (slot-value layer 'kernel i j session) (slot-value layer 'bias i j session) session))))))
+   (when (typep layer 'tensorflow:convolutional)
+     (dolist (filter (slot-value layer 'filters))
+       (dolist (i (range (slot-value layer 'kernel-size) (slot-value layer 'kernel-size) session))
+         (dolist (j (range (slot-value layer 'kernel-size) (slot-value layer 'kernel-size) session))
+           (tf:assign (slot-value layer 'output i j session) (tf:add (slot-value layer 'kernel i j session) (slot-value layer 'bias i j session) session))))
+       (dolist (i (range (slot-value layer 'kernel-size) (slot-value layer 'kernel-size) session))
+         (dolist (j (range (slot-value layer 'kernel-size) (slot-value layer 'kernel-size) session))
+           (tf:assign (slot-value layer 'output i j session) (tf:add (slot-value layer 'kernel i j session) (slot-value layer 'bias i j session) session)))))
+       (dolist (i (range (slot-value layer 'kernel-size) (slot-value layer 'kernel-size) session))
+         (dolist (j (range (slot-value layer 'kernel-size) (slot-value layer 'kernel-size) session))
+           (tf:assign (slot-value layer 'output i j session) (tf:add (slot-value layer 'kernel i j session) (slot-value layer 'bias i j session) session)))))))
+ (dolist (layer (tensorflow:layers gan))
+   (when (typep layer 'tensorflow:fully-connected)
+     (dolist (i (range (slot-value layer 'size) session))
+       (dolist (j (range (slot-value layer 'size) session))
+         (tf:assign (slot-value layer 'output i j session) (tf:add (slot-value layer 'kernel i j session) (slot-value layer 'bias i j session) session))))))
+   (when (typep layer 'tensorflow:dense)
+     (dolist (i (range (slot-value layer 'size) session))
+       (dolist (j (range (slot-value layer 'size) session))
+         (tf:assign (slot-value layer 'output i j session) (tf:add (slot-value layer 'kernel i j session) (slot-value layer 'bias i j session) session)))))))
+ (dolist (layer (tensorflow:layers gan))
+   (when (typep layer 'tensorflow:regression)
+     (dolist (i (range (slot-value layer 'size) session))
+       (dolist (j (range (slot-value layer 'size) session))
+         (tf:assign (slot-value layer 'output i j session) (tf:add (slot-value layer 'kernel i j session) (slot-value layer 'bias i j session) session)))))))
+ (dolist (layer (tensorflow:layers gan))
+   (when (typep layer 'tensorflow:classification)
+     (dolist (i (range (slot-value layer 'size) session))
+       (dolist (j (range (slot-value layer 'size) session))
+         (tf:assign (slot-value layer 'output i j session) (tf:add (slot-value layer 'kernel i j session) (slot-value layer 'bias i j session) session))))))))

DeepFakeDetectionSoftware/DeepFakeDetection/LispScripts/main.lisp

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+(in-package :deep-fake-preprocessing)
+(defpackage :preprocessing
+ (:use :cl)
+ (:export :preprocess-image))
+
+(in-package :preprocessing)
+defun preprocess-image (image-path)
+ "Main function to preprocess the image."
+    let* ((image (load-image image-path))
+         (normalized-image (normalize-image image))
+         (resized-image (resize-image normalized-image 1024 1024))
+         (image-path (merge-pathnames (pathname-namestring resized-image) :temp))
+         (save-image-function (intern (string-append "save-image-" (1+ (random 1000)) "." (pathname-extension resized-image))))
+    (setf (gd:image-save-function resized-image) save-image-function)
+    (funcall (symbol-function save-image-function) resized-image image-path)
+    (format t "Preprocessed image saved to ~A" image-path)
+    (values resized-image))
+
+(defun load-image (pathname)
+ "Load an image from a pathname."
+ (let ((image (gd:make-image (pathname-type pathname) :load-from pathname)))
+   (when (null image)
+     (error "Unable to load image ~A." pathname)
+     (return-from load-image nil))
+   (values image)))
+
+defun normalize-image (image)
+ "Normalize the image to a standard format and size."
+let* ((width (gd:image-width image))
+         (height (gd;image-height image))
+         (new-width 1024)
+         (new-height 1024)
+         (new-image (make-gd-image :width new-width :height new-height :depth (gd:image-depth image))))
+    (loop for x from 0 below new-width
+          for y from 0 below new-height
+          for pixmap = (gd:get-pixmap-data image)
+          for new-pixmap = (gd:get-pixmap-data new-image)
+          do (setf (aref new-pixmap x y) (aref pixmap (* (mod x width) (/ width width)) (* (mod y height) (/ height height)))))
+    (setf (gd:image-pixmap new-image) new-image)
+    new-image))
+
+(defun resize-image (image new-width new-height)
+ "Resize the image to the given width and height."
+ (let* ((image-width (gd:image-width image))
+        (image-height (gd:image-height image))
+        (new-image (make-gd-image :width new-width :height new-height :depth (gd:image-depth image))))
+    (cond ((< image-width new-width) (gd:fill-rectangle new-image 0 0 image-width (1- new-height) (min (1- (/ image-width image-width)) (1- (/ image-height new-height)))))
+         ((> image-width new-width) (gd:fill-rectangle new-image 0 0 (1- new-width) image-height (min (1- (/ image-width image-width)) (1- (/ image-height new-height)))))
+         ((< image-height new-height) (gd:fill-rectangle new-image 0 0 image-width (1- new-height) (min (1- (/ image-width image-width)) (1- (/ image-height new-height)))))
+         ((> image-height new-height) (gd:fill-rectangle new-image 0 0 (1- new-width) image-height (min (1- (/ image-width image-width)) (1- (/ image-height new-height))))))
+    (gd:image-save-copy new-image (pathname-name (merge-pathnames (pathname-namestring image) :temp)))
+    (setf (gd:image-pixmap new-image) new-image)
+    new-image))
+
+defun save-image (image-name)
+ "Save an image to a file."
+ (let* ((image (gd:make-image (pathname-type image-name) :load-from image-name)))
+   (when (null image)
+     (error "Unable to load image ~A." image-name)
+     (return-from save-image nil))
+   (let* ((new-image (make-gd-image :width 1024 :height 1024 :depth (gd:image-depth image))))
+     (loop for x from 0 below 1024
+           for y from 0 below 1024
+           for pixmap = (gd:get-pixmap-data image)
+           for new-pixmap = (gd:get-pixmap-data new-image)
+           do (setf (aref new-pixmap x y) (aref pixmap (/ (mod x (gd:image-width image)) (gd:image-width image)) (/ (mod y (gd:image-height image)) (gd:image-height image)))))
+     (setf (gd:image-pixmap new-image) new-image)
+     (gd:image-save new-image (pathname-name (merge-pathnames image-name :temp)))
+     (gd:image-free image)
+     (gd:image-free new-image)
+     t))
+
+(defun display-image (image-name)
+ "Display an image on the screen."
+ (let* ((image (gd:make-image (pathname-type image-name) :load-from image-name)))
+    (when (null image)
+      (error "Unable to load image ~A." image-name)
+      (return-from display-image nil))
+    (let* ((screen (gd:gd-open-display 1 1))
+           (screen-width (gd:gd-display-width screen))
+           (screen-height (gd:gd-display-height screen))
+           (image-width (gd:image-width image))
+           (image-height (gd:image-height image)))
+      (when (and (> image-width screen-width) (> image-height screen-height))
+        (error "Image is too large to fit on screen."))
+      (gd:gd-image-copy-to-display image screen 0 0)
+      (gd:gd-image-free image)
+      (gd:gd-close-display screen)))
+ t)
+
+defun display-image-for-seconds (image-name seconds &key (x 0) (y 0))
+ "Display the image for the given number of seconds."
+    let* ((image (gd:make-image (pathname-type image-name) :load-from image-name)))
+    (when (null image)
+      (error "Unable to load image ~A." image-name)
+      (return-from display-image-for-seconds nil))
+    let* ((screen (gd:gd-open-display 1 1))
+           (screen-width (gd:gd-display-width screen))
+           (screen-height (gd:gd-display-height screen))
+           (image-width (gd:image-width image))
+           (image-height (gd:image-height image)))
+      (when (and (> image-width screen-width) (> image-height screen-height))
+        (error "Image is too large to fit on screen."))
+      (setf (gd:gd-image-colormap screen) (gd:gd-default-colormap))
+      (gd:gd-image-copy-to-display image screen x y)
+      (gd:gd-image-free image)
+      (gd:usleep (/ 1000 seconds))
+      (gd:gd-image_flip_page screen)
+      (gd:gd-image_set_palette screen 1)
+      (gd:gd-image_set_resolution screen 1)
+      (gd:gd-image_set_color_map screen (gd:gd-default-colormap))
+      (gd:gd-image_set_bpp screen 8)
+      (gd:gd-image_set_max_red screen 0)
+      (gd:gd-image_set_max_green screen 0)
+      (gd:gd-image_set_max_blue screen 0)
+      (gd:gd-image_set_gd_flags screen)
+      (gd:gd-image_set_dither screen)
+      (gd:gd-image_set_type screen)
+      (gd:gd-image_set_x_pixels screen 0)
+      (gd:gd-image_set_y_pixels screen 0)
+      (gd:gd-image_set_x_spacing screen 0)
+      (gd:gd-image_set_y_spacing screen 0)
+      (gd:gd-image_set_x_resolution screen 0)
+      (gd:gd-image_set_y_resolution screen 0)
+      (gd:gd-image_set_alpha screen 0)
+      (gd:gd-image_set_interlace screen)
+      (gd:gd-image_set_transparent_color screen 0 0 0)
+      (gd:gd-image_set_palettes screen)
+      (gd:gd-image_set_palette_n_colors screen)
+      (gd:gd-image_set_palette_red screen)
+      (gd:gd-image_set_palette_green screen)
+      (gd:gd-image_set_palette_blue screen)
+      (gd:gd-image_set_palette_flag screen)
+      (gd:gd-image_set_palette_entry screen)
+      (gd:gd-image_set_palette_entry_flags screen)
+      (gd:gd-image_set_palette_rle screen)
+      (gd:gd-image_set_palette_info screen)
+      (gd:gd-image_set_palette_info_flags screen)
+      (gd:gd-image_set_palette_info_raw screen)
+      (gd:gd-image_set_palette_info_raw_flags screen)
+      (gd:gd-image_set_palette_raw screen)
+      (gd:gd-image_set_palette_raw_flags screen)
+      (gd:gd-image_set_palette_info_raw_flags screen)
+      (gd:gd-image_set_palette_info_flags screen)
+      (gd:gd-image_set_palette_rle screen)
+      (gd:gd-image_set_palette_flags screen)
+      (gd:gd-image_set_palette_entry_flags screen)
+      (gd:gd-image_set_palette_entry screen)
+      (gd:gd-image_set_palette_entry_color screen)
+      (gd:gd-image_set_palette_entry_flags screen)
+      (gd:gd-image_set_palette_rle_flag screen)
+      (gd:gd-image_set_palette_info_raw_flag screen)
+      (gd:gd-image_set_palette_info_flags screen)
+      (gd:gd-image_set_palette_info_raw screen)
+      (gd:gd-image_set_palette_info screen)
+      (gd:gd-image_set_palette_entry_raw screen)
+      (gd:gd-image_set_palette_entry_flags screen)
+      (gd:gd-image_set_palette_entry_color screen)
+      (gd:gd-image_set_palette_entry screen)
+      (gd:gd-image_set_palette_entry_raw screen)
+      (gd:gd-image_set_palette_entry_flags screen)
+      (gd:gd-image_set_palette_entry_color screen)
+      (gd:gd-image_set_palette_entry screen)
+      (gd:gd-image_set_palette_entry_raw screen)
+      (gd:gd-image_set_palette_entry_flags screen)
+      (gd:gd-image_set_palette_entry_color screen)
+      (gd:gd-image_set_palette_entry screen)
+      (gd:gd-image_set_palette_entry_raw screen)
+      (gd:gd-image_set_palette_entry_flags screen)
+      (gd:gd-image_set_palette_entry_color screen)
+      (gd:gd-image_set_palette_entry screen)
+      (gd:gd-image_set_palette_entry_raw screen)
+      (gd:gd-image_set_palette_entry_flags screen)
+      (gd:gd-image_set_palette_entry_color screen)
+      (gd:gd-image_set_palette_entry screen)
+      (gd:gd-image_set_palette_entry_raw screen)
+      (gd:gd-image_set_palette_entry_flags screen)
+      (gd:gd-image_set_palette_entry_color screen)
+      (gd:gd-image_set_palette_entry screen)
+      (gd:gd-image_set_palette_entry_raw screen)
+      (gd:gd-image_set_palette_entry_flags screen)
+      (gd:gd-image_set_palette_entry_color screen)
+      (gd:gd-image_set_palette_entry screen)
+      (gd:gd-image_set_palette_entry_raw screen)
+      (gd:gd-image_set_palette_entry_flags screen)
+      (gd:gd-image_set_palette_entry_color screen)
+      (gd:gd-image_set_palette_entry screen)
+      (gd:gd-image_set_palette_entry_raw screen)

DeepFakeDetectionSoftware/DeepFakeDetection/LispScripts/normalize.lisp

@@ -0,0 +1,21 @@
+(in-package :deep-fake-preprocessing)
+(defpackage :cl-gd
+ (:use :cl)
+ (:export :load-gd-image
+           :load-gd-image-with-data
+           :save-gd-image))
+
+(defun normalize-image (image)
+ "Normalize the image to a standard format and size."
+ (let* ((width (gd:image-width image))
+         (height (gd:image-height image))
+         (new-width 1024)
+         (new-height 1024)
+         (new-image (make-gd-image :width new-width :height new-height :depth (gd:image-depth image))))
+    (loop for x from 0 below new-width
+          for y from 0 below new-height
+          for pixmap = (gd:get-pixmap-data image)
+          for new-pixmap = (gd:get-pixmap-data new-image)
+          do (setf (aref new-pixmap x y) (aref pixmap (* (mod x width) (/ width width)) (* (mod y height) (/ height height)))))
+    (setf (gd:image-pixmap new-image) new-image)
+    new-image))

DeepFakeDetectionSoftware/DeepFakeDetection/LispScripts/preprocessing.lisp

@@ -0,0 +1,75 @@
+(defpackage :deep-fake-preprocessing
+ (:use :cl)
+ (:import-from :cl-image-processing :*image-readers* :read-image))
+(in-package :deep-fake-preprocessing)
+
+(defparameter *image-readers* (append *image-readers* '("gan-image-reader" :description "Reads images using GAN preprocessing.")))
+
+(defun load-image (path &key (reader (find-if #'stringp (car *image-readers*)) :key-restart (error "Unknown image reader: ~A" reader)) (options (car (assoc reader *image-readers*))) :allow-extension t)
+ "Load an image from the given path using the specified image reader."
+ (read-image (car *image-readers*) path reader options))
+
+(defun normalize-image (image)
+ "Normalize the image to a standard format and size."
+ (let ((new-image (make-array (car (image-size image)) :initial-contents nil :element-type (type-difference (car (image-pixel-type image)) 'double-float))))
+    (loop for y below (cadr (image-size image))
+          for x below (car (image-size image))
+          do (setf (schar new-image x y) (schar image x y))))
+    new-image))
+
+(defun enhance-image (image)
+ "Apply enhancements to the image to improve its quality for analysis."
+ (let ((new-image (make-array (car (image-size image)) :initial-contents nil :element-type (type-difference (car (image-pixel-type image)) 'double-float))))
+    (loop for y below (cadr (image-size image))
+          for x below (car (image-size image))
+          do (setf (schar new-image x y) (schar image x y))))
+    new-image))
+
+(defun preprocess-image (image-path)
+ "Main function to preprocess the image using GAN-based techniques."
+ (let ((image (load-image image-path :reader "gan-image-reader" :options '(:restore t))))
+   (setf image (normalize-image image))
+   (let ((new-image (deep-fake-enhance image)))
+     (setf new-image (enhance-image new-image))
+     (save-image new-image "path/to/preprocessed-image.jpg")))
+   
+(defun deep-fake-enhance (image)
+ "Enhance the image using GAN-based techniques."
+ (let* ((generator (make-instance 'gan-generator))
+        (discriminator (make-instance 'gan-discriminator)))
+   (dolist (batch (deep-fake-generator-training-batch generator discriminator))
+     (deep-fake-generator-train generator batch))
+   (deep-fake-discriminator-train discriminator image)
+   (let ((new-image (make-array (car (image-size image)) :initial-contents nil :element-type (type-difference (car (image-pixel-type image)) 'double-float))))
+     (loop for y below (cadr (image-size image))
+           for x below (car (image-size image))
+           do (setf (schar new-image x y) (schar image x y))))
+   new-image))
+
+(defun deep-fake-generator-training-batch (generator &key (batch-size 1024) (num-iter 1000) (noise-scale 0.05))
+ "Generate a training batch for the generator using GANs."
+ (loop with rnd = (make-instance 'random-state)
+        for i from 1 below batch-size
+        collect (let ((x (make-array (car (image-size generator)) :initial-contents nil :element-type (type-difference (car (image-pixel-type generator)) 'double-float))))
+                 (loop for y below (cadr (image-size generator))
+                       for x below (car (image-size generator))
+                       do (setf (schar x x y) (random rnd :minus 1 :plus 1))))))
+
+(defun deep-fake-discriminator-train (discriminator batch &key (num-iter 1000) (noise-scale 0.05))
+ "Train the discriminator using GANs."
+ (loop for i from 1 below num-iter
+        do (let ((loss (deep-fake-discriminator-loss discriminator batch)))
+            (when (> (random-real *standard-random* :a 0.5 :b 1.0) loss)
+              (deep-fake-generator-train (generator discriminator) batch (random-keyword-arguments (random-keywords :noise-scale 0.05 :num-iter 1000) :noise-scale (1-noise-scale) :num-iter (1-num-iter)))))))
+
+(defun deep-fake-discriminator-loss (discriminator batch)
+ "Calculate the loss of the discriminator when trained against the batch."
+ (let ((true-images (mapcar #'array-to-image batch))
+       (predicted-images (mapcar (lambda (x) (mapcar (lambda (y) (schar x y)) x)) true-images)))
+       (correct-predictions (mapcar (lambda (x) (mapcar (lambda (y) (zerop (schar x y)))) x)) true-images))
+       (incorrect-predictions (mapcar (lambda (x) (mapcar (lambda (y) (minusp (schar x y)))) x)) true-images)))
+   (let ((true-sum (apply #'sum (map (lambda (x) (map (lambda (y) (schar x y)) x)) true-images)))))
+       (predicted-sum (apply #'sum (map (lambda (x) (map (lambda (y) (schar x y)) x)) predicted-images)))))
+       (correct-count (apply #'sum (map (lambda (x) (map (lambda (y) (zerop (schar x y)))) x)) true-images)))
+       (incorrect-count (apply #'sum (map (lambda (x) (map (lambda (y) (minusp (schar x y)))) x)) true-images)))
+       (loss (- (/ predicted-sum true-sum) (/ correct-count (length true-images)) (/ incorrect-count (length true-images)))))))

DeepFakeDetectionSoftware/DeepFakeDetection/LispScripts/resize.lisp

@@ -0,0 +1,110 @@
+(in-package :DEEPFACE)
+
+(defpackage :deep-fake-resizing
+ (:use :cl))
+(in-package :deep-fake-resizing)
+
+(defun resize-image (input-path output-path)
+ "Resize the input image to the output size while maintaining its aspect ratio."
+ (let* ((image (load-image input-path))
+         (w (car image) :in-pattern :sew)
+         (h (cddr image) :in-pattern :sew)
+         (new-w (car output-path))
+         (new-h (cddr output-path)))
+    (assert (and (numberp new-w) (numberp new-h)))
+    (assert (<= new-w (expt 2 8)))
+    (assert (<= new-h (expt 2 8)))
+    (let ((new-image (make-array (list (ceiling (truncate new-w / (expt 2 8))) (ceiling (truncate new-h / (expt 2 8)))) :element-type '(unsigned-byte 8))))
+      (dotimes (i (truncate new-w / (expt 2 8)))
+        (dotimes (j (truncate new-h / (expt 2 8)))
+          (setf (aref new-image i j) (aref (aref image 0) i j))))
+      (save-image new-image output-path :filter 'cmu-fast-lanczos-resample)
+      
+      
+      (release-image new-image))))
+
+(defun save-image (image path &key (filter 'bicubic) (color-space 'srgb2))
+ "Save the image to the given path."
+ (with-open-file (stream path
+                        :direction :output
+                        :if-exists :replace
+                        :formatting :unreadable
+                        :element-type '(unsigned-byte 8))
+   (dotimes (i (length image))
+     (write-sequence (aref image i) stream))))
+
+(defun release-image (image)
+ "Release the memory used by the image."
+ (setf image nil)
+ (garbage-collect))
+
+(defun load-image (path)
+ "Load an image from the given path."
+ (when (probe-file path)
+   (with-open-file (stream path
+                          :direction :input
+                          :if-does-not-exist :error
+                          :element-type '(unsigned-byte 8))
+     (let ((image (make-array (file-length stream) :element-type '(unsigned-byte 8))))
+       (dotimes (i (file-length stream))
+         (setf (aref image i) (read-sequence (unsigned-byte 8) stream)))
+       image))))
+
+(defun main ()
+ "The main function for the resize script."
+ (interactive)
+ (let* ((input-path (read-from-string "Enter input image path: ") :in-package :DEEPFACE)
+        (output-path (read-from-string "Enter output image path: ") :in-package :DEEPFACE))
+   (if (null input-path)
+       (progn
+         (print "You must specify an input image path." :in-package :DEEPFACE)
+         (return-from main nil))
+       (if (null output-path)
+           (progn
+             (print "You must specify an output image path." :in-package :DEEPFACE)
+             (return-from main nil))
+           (progn
+             (resize-image input-path output-path))))))
+
+
+
+(defun cmu-fast-lanczos-resample (image)
+ "Perform a Lanczos resampling filter on the image."
+ (when (consp image)
+   (let ((image (cdr image))
+         (filter (caar image)))
+     (if (eq filter 'cmu-fast-lanczos-resample)
+         image
+         (error "Unknown filter: ~s" filter))))
+   
+   (if (zerop (car image))
+       (error "Zero size image." :in-package :DEEPFACE)
+       (let ((new-size (expt 2 (1- (log (car image)) 8))))
+         (setf (car image) new-size)
+         (cmu-fast-lanczos-resample image))))
+
+         (in-package :deep-fake-preprocessing)
+(defpackage :cl-gd
+ (:use :cl)
+ (:export :load-gd-image
+           :load-gd-image-with-data
+           :save-gd-image))
+
+defun resize-image (image new-width new-height)
+ "Resize the image to the given width and height."
+ (let* ((image-width (gd:image-width image))
+        (image-height (gd:image-height image))
+        (new-image (make-gd-image :width new-width :height new-height :depth (gd:image-depth image))))
+    (flet ((fill-area (x y w h)
+           (declare (type fixnum x y w h))
+           (dotimes (n h)
+             (dotimes (m w)
+               (setf (aref (gd:get-pixmap-data new-image) m n)
+                   (min (1- (/ w image-width))
+                       (1- (/ h image-height))))))))
+      (cond ((< image-width new-width) (fill-area (1- new-width) 0 (image-width) image-height))
+            ((> image-width new-width) (fill-area 0 0 (image-width) (1- new-height)))
+            ((< image-height new-height) (fill-area 0 0 image-width (1- new-height)))
+            ((> image-height new-height) (fill-area (1- new-width) 0 image-width (1- new-height))))
+      (setf (gd:image-pixmap new-image) new-image)
+      new-image))

DeepFakeDetectionSoftware/DeepFakeDetection/main.py

@@ -0,0 +1,80 @@
+import subprocess
+import json
+
+def call_lisp_script(script_path, input_data):
+    # Convert input data to JSON
+    input_json = json.dumps(input_data)
+    
+    # Call Lisp script with subprocess
+    result = subprocess.run(['lisp', script_path], input=bytes(input_json, 'utf-8'), capture_output=True)
+    
+    # Parse the output from Lisp script
+    output_data = json.loads(result.stdout)
+    
+    return output_data
+
+def load_image(image_path):
+    # Call preprocessing Lisp script
+    return call_lisp_script('LispScripts/preprocessing.lisp', {'image_path': image_path})
+
+def resize_image(image, width, height):
+    # Call resize Lisp script
+    return call_lisp_script('LispScripts/resize.lisp', {'image': image, 'width': width, 'height': height})
+
+def normalize_image(image):
+    # Call normalize Lisp script
+    return call_lisp_script('path_to_normalize.lisp', {'image': image})
+
+def inpaint_image(image, mask):
+    # Call inpainting Lisp script
+    return call_lisp_script('LispScripts/inpainting.lisp', {'image': image, 'mask': mask})
+def inpaint_image(image, mask):
+    # Call inpainting Lisp script
+    return call_lisp_script('LispScripts/main.lisp', {'image': image, 'mask': mask})
+
+def restore_image(image):
+    # Call restoration Lisp script
+    return call_lisp_script('LispScripts/image_restoration.lisp', {'image': image})
+
+def generate_image_with_gan(gan_input_data):
+    # Call GAN Lisp script
+    return call_lisp_script('LispScripts/gan.lisp', {'gan_input_data': gan_input_data})
+
+def extract_features(image):
+    # Call feature extraction Lisp script
+    return call_lisp_script('LispScripts/feature_extraction.lisp', {'image': image})
+
+def main():
+    # Path to the image file
+    image_path = 'path_to_image.jpg'
+    
+    # Load and preprocess the image
+    image = load_image(image_path)
+    resized_image = resize_image(image, 256, 256)
+    normalized_image = normalize_image(resized_image)
+    
+    # Inpainting and restoration
+    mask = 'path_to_mask.png'
+    inpainted_image = inpaint_image(normalized_image, mask)
+    restored_image = restore_image(inpainted_image)
+
+    # GAN generation and feature extraction
+    gan_input_data = 'path_to_gan_input_data.txt'
+    generated_image = generate_image_with_gan(gan_input_data)
+    features = extract_features(restored_image)
+    
+    return features
+def call_lisp_script(script_path, input_data):
+    # Convert input data to JSON
+    input_json = json.dumps(input_data)
+    
+    # Replace 'lisp' with the correct command for your Lisp interpreter (e.g., 'sbcl')
+    result = subprocess.run(['sbcl', '--script', script_path], input=bytes(input_json, 'utf-8'), capture_output=True)
+    
+    # Parse the output from Lisp script
+    output_data = json.loads(result.stdout)
+    
+    return output_data
+
+if __name__ == '__main__':
+    main()