True vs Fake¶

Face recognition DataSet¶

Remember to follow me on GitHub for more DataSets and more 😉

We need to import all the differents libraries¶

In [1]:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers, models
from keras.datasets import fashion_mnist
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD
from keras.utils import to_categorical
import os
import matplotlib.pyplot as plt
import numpy as np
#import cv2 as cv
import tensorflow as tf
from concurrent.futures import ThreadPoolExecutor

2024-05-09 16:09:52.602817: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-05-09 16:09:52.602927: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-05-09 16:09:52.705541: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered

We define the path of the Dataset with¶

  • Train
  • Test
  • Validation
In [2]:
import pathlib

#path=str(pathlib.Path().resolve())+"\\Dataset"
path = "/kaggle/input/deepfake-and-real-images/Dataset"

Optional (If you want you can use the GPU just checking if It's avaliable)

In [3]:
tf.test.is_gpu_available()
Out[3]:
True

We recover the Data from the Path as Tensor from Tensorflow to improve the Speed¶

In [4]:
def get_from_dir(dir):
    global path
    dir = os.path.join(path,dir)
    return tf.keras.utils.image_dataset_from_directory(
           dir,
           labels='inferred',
           color_mode="rgb",
           seed=42,
           batch_size=32,
           image_size=(128, 128))

train = get_from_dir("Train")
test = get_from_dir("Test")
val = get_from_dir("Validation")

Found 140002 files belonging to 2 classes.
Found 10905 files belonging to 2 classes.
Found 39428 files belonging to 2 classes.

Now we are going to check an example of images in the Train Data¶

In [5]:
from matplotlib import pyplot as plt

class_names = np.unique(train.class_names)

n_rows = 4
n_cols = 10
plt.figure(figsize=(n_cols * 1.2, n_rows * 1.2))
data = list(train.take(n_rows*n_cols))
for row in range(n_rows):
    for col in range(n_cols):
        index = n_cols * row + col
        # Obtener solo la primera imagen del lote
        single_image = data[index][0][0]  # Tomar la primera imagen del primer lote
        plt.subplot(n_rows, n_cols, index + 1)
        plt.imshow(single_image.numpy().astype("uint8"),cmap="gray")  # Convertir a tipo uint8 para imshow
        plt.axis('off')
        # Convertir a un solo valor antes de usarlo para indexar class_names
        label_index = data[index][1][0]
        plt.title(class_names[label_index], fontsize=12)
plt.subplots_adjust(wspace=0.2, hspace=0.5)
plt.show()

Model Processing¶

We are going to define our model¶

The model use the next neural layers:

  • 2x Conv2D (32, activation="relu")
  • 1x Dropout (0.5)
  • 1x MaxPooling2D((2,2), strides=(2,2))
  • 2x Conv2D (64, activation="relu")
  • 1x Dropout (0.5)
  • 1x MaxPooling2D((2,2), strides=(2,2))
  • 2x Conv2D (128, activation="relu")
  • 1x Dropout (0.5)
  • 1x MaxPooling2D((2,2), strides=(2,2))
  • 1x Flatten
  • 1x Dropout (0.5)
  • 1x Dense (128, activation="relu")
  • 1x Dense (256, activation="relu")
  • 1x Dense (1, activation="sigmoid")
In [6]:
model = models.Sequential()


model.add(layers.Conv2D(32,(3,3),activation="relu", padding='same',input_shape=(128,128,3)))
model.add(layers.Conv2D(32,(3,3),activation="relu", padding='same'))
model.add(layers.MaxPooling2D((2,2), strides=(2, 2)))


model.add(layers.Conv2D(64,(3,3),activation="relu", padding='same'))
model.add(layers.Conv2D(64,(3,3),activation="relu", padding='same'))
model.add(layers.MaxPooling2D((2,2), strides=(2, 2)))


model.add(layers.Conv2D(128,(3,3),activation="relu", padding='same'))
model.add(layers.Conv2D(128,(3,3),activation="relu", padding='same'))
model.add(layers.MaxPooling2D((2,2), strides=(2, 2)))

model.add(layers.Flatten())
model.add(layers.Dropout(0.5))
model.add(layers.Dense(128,activation="relu"))
model.add(layers.Dense(256,activation="relu"))
model.add(layers.Dense(1,activation="sigmoid"))

model.summary()

/opt/conda/lib/python3.10/site-packages/keras/src/layers/convolutional/base_conv.py:99: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv2d (Conv2D)                 │ (None, 128, 128, 32)   │           896 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv2d_1 (Conv2D)               │ (None, 128, 128, 32)   │         9,248 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ max_pooling2d (MaxPooling2D)    │ (None, 64, 64, 32)     │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv2d_2 (Conv2D)               │ (None, 64, 64, 64)     │        18,496 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv2d_3 (Conv2D)               │ (None, 64, 64, 64)     │        36,928 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ max_pooling2d_1 (MaxPooling2D)  │ (None, 32, 32, 64)     │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv2d_4 (Conv2D)               │ (None, 32, 32, 128)    │        73,856 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv2d_5 (Conv2D)               │ (None, 32, 32, 128)    │       147,584 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ max_pooling2d_2 (MaxPooling2D)  │ (None, 16, 16, 128)    │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ flatten (Flatten)               │ (None, 32768)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 32768)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 128)            │     4,194,432 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_1 (Dense)                 │ (None, 256)            │        33,024 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_2 (Dense)                 │ (None, 1)              │           257 │
└─────────────────────────────────┴────────────────────────┴───────────────┘

 Total params: 4,514,721 (17.22 MB)

 Trainable params: 4,514,721 (17.22 MB)

 Non-trainable params: 0 (0.00 B)

We compile our model as BinaryCrossentropy with Adam¶

In [7]:
model.compile('adam',loss=tf.keras.losses.BinaryCrossentropy(), metrics=['accuracy'])

Now we are going to train our model using the validation data, this will prevent our model to over train and memorize the data that has been given¶

In [8]:
#gpus = tf.config.experimental.list_physical_devices('GPU')
#print(gpus)
#tf.config.set_visible_devices([], 'CPU') # hide the CPU
#tf.config.set_visible_devices(gpus[0], 'GPU') # unhide potentially hidden GPU
#tf.config.get_visible_devices()


#cpus = tf.config.experimental.list_physical_devices('CPU')
#print(cpus)
#tf.config.set_visible_devices([], 'GPU')  # hide the GPU
#tf.config.set_visible_devices(cpus[0], 'CPU') # unhide potentially hidden CPU
#tf.config.get_visible_devices()

early_stopping_cb = keras.callbacks.EarlyStopping(patience=5,restore_best_weights=True)


history = model.fit(train,epochs=20,batch_size=32,validation_data=val,callbacks=[early_stopping_cb])

Epoch 1/20
   3/4376 ━━━━━━━━━━━━━━━━━━━━ 4:30 62ms/step - accuracy: 0.5243 - loss: 25.8930
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1715271154.504960      79 device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.
W0000 00:00:1715271154.527023      79 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update

4376/4376 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step - accuracy: 0.7799 - loss: 0.6101
W0000 00:00:1715271329.680104      76 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update
W0000 00:00:1715271330.676683      79 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update

4376/4376 ━━━━━━━━━━━━━━━━━━━━ 240s 51ms/step - accuracy: 0.7800 - loss: 0.6100 - val_accuracy: 0.8603 - val_loss: 0.3689
Epoch 2/20
4376/4376 ━━━━━━━━━━━━━━━━━━━━ 186s 42ms/step - accuracy: 0.9252 - loss: 0.1863 - val_accuracy: 0.9166 - val_loss: 0.2041
Epoch 3/20
4376/4376 ━━━━━━━━━━━━━━━━━━━━ 186s 42ms/step - accuracy: 0.9420 - loss: 0.1443 - val_accuracy: 0.9322 - val_loss: 0.1670
Epoch 4/20
4376/4376 ━━━━━━━━━━━━━━━━━━━━ 185s 42ms/step - accuracy: 0.9528 - loss: 0.1213 - val_accuracy: 0.9369 - val_loss: 0.1668
Epoch 5/20
4376/4376 ━━━━━━━━━━━━━━━━━━━━ 186s 42ms/step - accuracy: 0.9560 - loss: 0.1163 - val_accuracy: 0.9356 - val_loss: 0.1530
Epoch 6/20
4376/4376 ━━━━━━━━━━━━━━━━━━━━ 185s 42ms/step - accuracy: 0.9628 - loss: 0.0974 - val_accuracy: 0.9372 - val_loss: 0.1669
Epoch 7/20
4376/4376 ━━━━━━━━━━━━━━━━━━━━ 205s 43ms/step - accuracy: 0.9635 - loss: 0.0957 - val_accuracy: 0.9380 - val_loss: 0.1528
Epoch 8/20
4376/4376 ━━━━━━━━━━━━━━━━━━━━ 203s 43ms/step - accuracy: 0.9662 - loss: 0.0884 - val_accuracy: 0.9409 - val_loss: 0.1509
Epoch 9/20
4376/4376 ━━━━━━━━━━━━━━━━━━━━ 186s 43ms/step - accuracy: 0.9700 - loss: 0.0801 - val_accuracy: 0.9495 - val_loss: 0.1591
Epoch 10/20
4376/4376 ━━━━━━━━━━━━━━━━━━━━ 190s 43ms/step - accuracy: 0.9689 - loss: 0.0810 - val_accuracy: 0.9275 - val_loss: 0.2339
Epoch 11/20
4376/4376 ━━━━━━━━━━━━━━━━━━━━ 188s 43ms/step - accuracy: 0.9622 - loss: 0.0985 - val_accuracy: 0.9416 - val_loss: 0.1560
Epoch 12/20
4376/4376 ━━━━━━━━━━━━━━━━━━━━ 188s 43ms/step - accuracy: 0.9669 - loss: 0.0859 - val_accuracy: 0.9496 - val_loss: 0.2221
Epoch 13/20
4376/4376 ━━━━━━━━━━━━━━━━━━━━ 188s 43ms/step - accuracy: 0.9645 - loss: 0.0923 - val_accuracy: 0.8984 - val_loss: 0.2253

Now we will Plot the history of the import matplotlib.pyplot as plt¶

In [9]:
pd.DataFrame(history.history).plot(figsize=(8, 5))
plt.grid(True)
plt.show()

A little evalutaion of it with the test data¶

In [10]:
model.evaluate(test)

341/341 ━━━━━━━━━━━━━━━━━━━━ 18s 51ms/step - accuracy: 0.9232 - loss: 0.1892
Out[10]:
[0.1964750587940216, 0.9223291873931885]
In [11]:
model.save("modelo_detector_caras.keras")

And with this Confussion Matrix we are going to Plot the results with the Test Data¶

In [12]:
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay


true_labels = []
predictions = []

for images, labels in test:
    true_labels.extend(labels.numpy())
    batch_predictions = model.predict(images) >= 0.5
    predictions.extend(batch_predictions.astype(int).flatten())

true_labels = np.array(true_labels)
predictions = np.array(predictions)

cm = confusion_matrix(true_labels, predictions)
disp = ConfusionMatrixDisplay(confusion_matrix=cm)
disp.plot()
plt.show()

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W0000 00:00:1715273714.032984      77 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update

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