{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":16880,"databundleVersionId":858837,"sourceType":"competition"},{"sourceId":924245,"sourceType":"datasetVersion","datasetId":464091}],"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"## Importing Required libraries","metadata":{"id":"dFXIv9qNpKzt","tags":[]}},{"cell_type":"code","source":"!python --version\n!pip --version","metadata":{"execution":{"iopub.status.busy":"2024-04-21T13:20:09.00383Z","iopub.execute_input":"2024-04-21T13:20:09.004179Z","iopub.status.idle":"2024-04-21T13:20:11.497242Z","shell.execute_reply.started":"2024-04-21T13:20:09.00415Z","shell.execute_reply":"2024-04-21T13:20:11.496273Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!pip install -U --upgrade tensorflow","metadata":{"execution":{"iopub.status.busy":"2024-04-21T13:20:11.499121Z","iopub.execute_input":"2024-04-21T13:20:11.499389Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pip install plotly","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pip install numpy==1.23.0  # Replace 1.23.0 with the version you want to install","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import sys\nimport sklearn\nimport tensorflow as tf\n\nimport cv2\nimport pandas as pd\nimport numpy as np\n\nimport plotly.graph_objs as go\nfrom plotly.offline import iplot\nfrom matplotlib import pyplot as plt","metadata":{"id":"TFSU3FCOpKzu","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tf.test.is_gpu_available()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tf.__version__","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\nplt.rc('font', size=14)\nplt.rc('axes', labelsize=14, titlesize=14)\nplt.rc('legend', fontsize=14)\nplt.rc('xtick', labelsize=10)\nplt.rc('ytick', labelsize=10)","metadata":{"id":"8d4TH3NbpKzx","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Data Visualisation","metadata":{"id":"NL3Ht4wC9b3n"}},{"cell_type":"code","source":"import os\n\ndef get_data():\n    return pd.read_csv('../input/deepfake-faces/metadata.csv')","metadata":{"id":"jfv9PxSB4tM8","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"meta=get_data()\nmeta.head()","metadata":{"id":"tDW7BRph9ehF","outputId":"97de18b5-0a37-4302-8804-8a16a7d2ed2f","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"meta.shape","metadata":{"id":"n7FSdDifbZxn","outputId":"5451a127-405a-4c0b-a197-c920b796adbb","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"len(meta[meta.label=='FAKE']),len(meta[meta.label=='REAL'])","metadata":{"id":"_FJcz2IthxVG","outputId":"274c3f65-7acb-4f99-8aa9-a5b2a23bf06a","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"real_df = meta[meta[\"label\"] == \"REAL\"]\nfake_df = meta[meta[\"label\"] == \"FAKE\"]\n\nsample_size = 8000\n\n# select 8000 random samples for each one\n# random_state=42: ensure if we run the code again, we'll get the same random results\nreal_df = real_df.sample(sample_size, random_state=42)\nfake_df = fake_df.sample(sample_size, random_state=42)\n\nsample_meta = pd.concat([real_df, fake_df])","metadata":{"id":"IgMfzY-PjjtH","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"As mentioned instead of using 95k images we will only use 16000 images.","metadata":{}},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\n\n# test_size=0.2: 20% of data is used for testing\n# stratify=sample_meta['label']: ensure that the percentage of real and fake samples in the testing set is similar to that in the original dataset\nTrain_set, Test_set = train_test_split(sample_meta,test_size=0.2,random_state=42,stratify=sample_meta['label'])\n# split train_set into a smaller training set (Train_set) and a validation set (Val_set), 30% used for val\nTrain_set, Val_set  = train_test_split(Train_set,test_size=0.3,random_state=42,stratify=Train_set['label'])","metadata":{"id":"5eB86S6K-T5Z","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"Train_set.shape,Val_set.shape,Test_set.shape","metadata":{"id":"8p-TONijb4qA","outputId":"56d0b529-9d81-4019-d8fa-618c8cdba90f","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y = dict()\n\n#  store the counts for REAL (y[0]) and FAKE (y[1]) classes.\ny[0] = []\ny[1] = []\n\nfor set_name in (np.array(Train_set['label']), np.array(Val_set['label']), np.array(Test_set['label'])):\n    y[0].append(np.sum(set_name == 'REAL'))\n    y[1].append(np.sum(set_name == 'FAKE'))\n\n#trace: a set of data points and the visual representation of that data on a graph or chart\ntrace0 = go.Bar(\n    x=['Train Set', 'Validation Set', 'Test Set'],\n    y=y[0],\n    name='REAL',\n    marker=dict(color='#33cc33'),\n    opacity=0.7\n)\ntrace1 = go.Bar(\n    x=['Train Set', 'Validation Set', 'Test Set'],\n    y=y[1],\n    name='FAKE',\n    marker=dict(color='#ff3300'),\n    opacity=0.7\n)\n\ndata = [trace0, trace1]\nlayout = go.Layout(\n    title='Count of classes in each set',\n    xaxis={'title': 'Set'},\n    yaxis={'title': 'Count'}\n)\n\nfig = go.Figure(data, layout)\niplot(fig)","metadata":{"id":"hzNGtCWd-mTk","outputId":"5178c3ed-cbba-4f99-99d0-26bde11a5dab","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The original image dataset were biased with more fake images than real since we are taking a sample of it its better to take equal proportion of real and fake images.","metadata":{}},{"cell_type":"code","source":"#  creates a new figure with a specified figure size of 15x15 inches.\nplt.figure(figsize=(15,15))\n# use enumerate to return set of index (cur) and value (i)\nfor cur,i in enumerate(Train_set.index[25:50]):\n#    trả về 1 ô có size (5x5 grid)\n    plt.subplot(5,5,cur+1)\n#      remove the tick marks, grid on the x and y axes.\n    plt.xticks([])\n    plt.yticks([])\n    plt.grid(False)\n#     This loads an image using OpenCV (cv2) and displays it in the subplot\n# The image path is constructed based on the filename stored in the videoname column of the Train_set\n    plt.imshow(cv2.imread('../input/deepfake-faces/faces_224/'+Train_set.loc[i,'videoname'][:-4]+'.jpg'))\n    \n    if(Train_set.loc[i,'label']=='FAKE'):\n        plt.xlabel('FAKE Image')\n    else:\n        plt.xlabel('REAL Image')\n        \nplt.show()","metadata":{"id":"VR7Uly2fcUYi","outputId":"c1f47a82-ef4f-4bcd-b51c-d4738142fc0f","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Modelling","metadata":{"id":"dOvN_divkl-N"}},{"cell_type":"markdown","source":"Before jumping to use pretrained model lets develop some base line model to test how our pretrained model outperforms.","metadata":{}},{"cell_type":"markdown","source":"### Custom CNN Architecture","metadata":{"id":"oid44Xx-pKz6"}},{"cell_type":"code","source":"# retrieves images and their corresponding labels from a DataFrame containing video names and their corresponding labels\n# set_name: video names and corresponding labels.\ndef retrieve_dataset(set_name):\n    images,labels=[],[]\n#     video names (img) and labels (imclass) \n    for (img, imclass) in zip(set_name['videoname'], set_name['label']):\n#         img[:-4]: remove the file extension\n        images.append(cv2.imread('../input/deepfake-faces/faces_224/'+img[:-4]+'.jpg'))\n        if(imclass=='FAKE'):\n            labels.append(1)\n        else:\n            labels.append(0)\n    \n    return np.array(images),np.array(labels)","metadata":{"id":"Hz0ZdQ_fgHhG","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# X: img, Y: label\nX_train,y_train=retrieve_dataset(Train_set)\nX_val,y_val=retrieve_dataset(Val_set)\nX_test,y_test=retrieve_dataset(Test_set)","metadata":{"id":"zeAGRcAbguKU","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from functools import partial\nimport tensorflow as tf\n# a \"seed\" is a starting point for the sequence of pseudo-random numbers\n#  ensuring that it produces the same sequence of random numbers each time we run the code with the same seed\ntf.random.set_seed(42) \n# creating convolutional layers\n# kernel: a small matrix of weights that is applied to the input data to extract features\n# padding=\"same\": input and output dimensions of the convolutional layer will be the same\n# kernel_initializer=\"he_normal\": The common weight initialization method\nDefaultConv2D = partial(tf.keras.layers.Conv2D, kernel_size=3, padding=\"same\",\n                        activation=\"relu\", kernel_initializer=\"he_normal\")\n# Define model\nmodel = tf.keras.Sequential([\n    # Input img: width:224, height:224, rgb:3\n    tf.keras.layers.Input(shape=[224, 224, 3]), \n    # Use Input layer with shape\n    # the first convolutional layer\n    DefaultConv2D(filters=64, kernel_size=7),\n#  a max pooling layer : down dimension\n    tf.keras.layers.MaxPool2D(),\n    DefaultConv2D(filters=128),\n    DefaultConv2D(filters=128),\n    tf.keras.layers.MaxPool2D(),\n#     Flattens the output of the convolutional layers into a 1D array.\n    tf.keras.layers.Flatten(),\n#     a fully connected (dense) layer\n    tf.keras.layers.Dense(units=128, activation=\"relu\",\n                          kernel_initializer=\"he_normal\"),\n    tf.keras.layers.Dropout(0.5),\n    tf.keras.layers.Dense(units=64, activation=\"relu\",\n                          kernel_initializer=\"he_normal\"),\n    tf.keras.layers.Dropout(0.5),\n    tf.keras.layers.Dense(units=1, activation=\"sigmoid\")\n])","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# This specifies the loss function to use during training\n# optimizer=\"nadam\": This specifies the optimizer to use for training\n# the evaluation metric to monitor during training\nmodel.compile(loss=\"binary_crossentropy\", optimizer=\"nadam\",\n              metrics=[\"accuracy\"])\nmodel.summary()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#  trains the compiled model and  evaluates it on the validation data\nhistory = model.fit(X_train, y_train, epochs=5,batch_size=64,\n                    validation_data=(X_val, y_val))","metadata":{"id":"KZbWeIBYpKz6","outputId":"deb6f56a-7b93-4241-a1bd-b210c0f2d426","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Assuming 'model' is your Keras model\nmodel.save('my_model.keras')\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#  return the test accuracy along with the test loss.\nscore = model.evaluate(X_test, y_test)","metadata":{"id":"6HDDr4uehast","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.metrics import accuracy_score, f1_score, confusion_matrix, roc_curve, auc\nimport matplotlib.pyplot as plt\n\n# Assuming you have predictions (y_pred) from your model\ny_pred = model.predict(X_test)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Assuming y_pred is a probability score, you may need to convert it to binary predictions\ny_pred_binary = (y_pred > 0.5).astype(int)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"accuracy = accuracy_score(y_test, y_pred_binary)\nprint(f'Accuracy: {accuracy}')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#  a common metric used to evaluate the performance of a binary classification model\nf1 = f1_score(y_test, y_pred_binary)\nprint(f'F1 Score: {f1}')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Generate confusion matrix: a table that is used to define the performance of a classification algorithm\nconf_matrix = confusion_matrix(y_test, y_pred_binary)\nprint('Confusion Matrix:')\nprint(conf_matrix)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Confusion matrix: \n![image.png](attachment:0b2d0236-4d13-4702-9087-241b9d8bf1d2.png)","metadata":{},"attachments":{"0b2d0236-4d13-4702-9087-241b9d8bf1d2.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"1.True Positive (TP) - Your model predicted the positive class. For example, identifying real img as real.\n\n2.True Negative (TN) - Your model correctly predicted the negative class. For example, identifying fake img as fake.\n\n3.False Positive (FP) - Your model incorrectly predicted the positive class. For example, identifying a fake img as real.\n\n4.False Negative (FN) - Your model incorrectly predicted the negative class. For example, identifying real img as a fake img.","metadata":{}},{"cell_type":"code","source":"pip install seaborn","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.metrics import confusion_matrix\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\n# Assuming y_test and y_pred_binary are defined\n\n# Generate confusion matrix: describe the performance of a classification model on a set of test data\n# especially in binary classification tasks, where the output can be classified into two classes (e.g., positive and negative).\nconf_matrix = confusion_matrix(y_test, y_pred_binary)\n\n# Plot confusion matrix as a heatmap\nplt.figure(figsize=(8, 6))\nsns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap=\"Blues\", cbar=False,\n            xticklabels=['Real', 'Fake'],\n            yticklabels=['Fake', 'Real'])\nplt.title('Confusion Matrix')\nplt.xlabel('Predicted')\nplt.ylabel('Actual')\nplt.show()\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"172: the number of instances correctly classified as \"Real\n\n1428: the number of instances incorrectly classified as \"Fake\" when they are actually \"Real\"\n\n144: the number of instances incorrectly classified as \"Real\" when they are actually \"Fake\"\n\n1456: the number of instances correctly classified as \"Fake\"","metadata":{}},{"cell_type":"code","source":"# Plot ROC curve\n#  false positive rate (FPR), true positive rate (TPR), and thresholds\nfpr, tpr, thresholds = roc_curve(y_test, y_pred)\n# Area Under the Curve \nroc_auc = auc(fpr, tpr)\n\nplt.figure(figsize=(8, 6))\nplt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (AUC = {roc_auc:.2f})')\nplt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\nplt.xlabel('False Positive Rate')\nplt.ylabel('True Positive Rate')\nplt.title('Receiver Operating Characteristic (ROC) Curve')\nplt.legend(loc='lower right')\nplt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.metrics import roc_curve, auc\n\n# Assuming y_test and y_pred are defined\nfpr, tpr, thresholds = roc_curve(y_test, y_pred)\n\n# Print TPR and FPR values\nfor i, (fpr_value, tpr_value) in enumerate(zip(fpr, tpr)):\n    print(f'Threshold: {thresholds[i]:.4f}, FPR: {fpr_value:.4f}, TPR: {tpr_value:.4f}')\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# plot model performance\nacc = history.history['accuracy']\nval_acc = history.history['val_accuracy']\nloss = history.history['loss']\nval_loss = history.history['val_loss']\nepochs_range = range(1, len(history.epoch) + 1)\n\nplt.figure(figsize=(15,5))\n\nplt.subplot(1, 2, 1)\nplt.plot(epochs_range, acc, label='Train Set')\nplt.plot(epochs_range, val_acc, label='Val Set')\nplt.legend(loc=\"best\")\nplt.xlabel('Epochs')\nplt.ylabel('Accuracy')\nplt.title('Model Accuracy')\n\nplt.subplot(1, 2, 2)\nplt.plot(epochs_range, loss, label='Train Set')\nplt.plot(epochs_range, val_loss, label='Val Set')\nplt.legend(loc=\"best\")\nplt.xlabel('Epochs')\nplt.ylabel('Loss')\nplt.title('Model Loss')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"A baseline score of 50.06% is good to go let's finetune some pretrained model","metadata":{}},{"cell_type":"markdown","source":"# Pretrained Models for Transfer Learning","metadata":{"id":"hqxnSBJ3pKz8"}},{"cell_type":"markdown","source":"Here i used Xception model for fine-tuning feel free to try the performance of other pretrained models.","metadata":{}},{"cell_type":"markdown","source":"All three datasets contain individual images. We need to batch them, but for this we first need to ensure they all have the same size, or else batching will not work. We can use a `Resizing` layer for this. We must also call the `tf.keras.applications.xception.preprocess_input()` function to preprocess the images appropriately for the Xception model. We will also add shuffling and prefetching to the training dataset.","metadata":{"id":"gXG6iv8XpKz9"}},{"cell_type":"code","source":"# creates TensorFlow Dataset objects\n# Dataset objects support various transformations and preprocessing steps, such as batching, shuffling, and augmentation\ntrain_set_raw=tf.data.Dataset.from_tensor_slices((X_train,y_train))\nvalid_set_raw=tf.data.Dataset.from_tensor_slices((X_val,y_val))\ntest_set_raw=tf.data.Dataset.from_tensor_slices((X_test,y_test))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# prepares TensorFlow Dataset objects for training, validation, and testing\ntf.keras.backend.clear_session()  # extra code – resets layer name counter\n\nbatch_size = 32\npreprocess = tf.keras.applications.xception.preprocess_input\ntrain_set = train_set_raw.map(lambda X, y: (preprocess(tf.cast(X, tf.float32)), y))\ntrain_set = train_set.shuffle(1000, seed=42).batch(batch_size).prefetch(1)\nvalid_set = valid_set_raw.map(lambda X, y: (preprocess(tf.cast(X, tf.float32)), y)).batch(batch_size)\ntest_set = test_set_raw.map(lambda X, y: (preprocess(tf.cast(X, tf.float32)), y)).batch(batch_size)","metadata":{"id":"Bnz0n9XApKz9","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let's take a look again at the first 9 images from the validation set: they're all with values ranging from -1 to 1:","metadata":{"id":"ovNEMky-pKz9"}},{"cell_type":"code","source":"# extra code – displays the first 9 images in the first batch of valid_set\n\nplt.figure(figsize=(12, 12))\nfor X_batch, y_batch in valid_set.take(1):\n    for index in range(9):\n        plt.subplot(3, 3, index + 1)\n        plt.imshow((X_batch[index] + 1) / 2)  # rescale to 0–1 for imshow()\n        if(y_batch[index]==1):\n            classt='FAKE'\n        else:\n            classt='REAL'\n        plt.title(f\"Class: {classt}\")\n        plt.axis(\"off\")\n\nplt.show()","metadata":{"id":"ZL3c3i4opKz9","outputId":"38847d8d-8822-41a3-cfb2-27479aa5debe","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data_augmentation = tf.keras.Sequential([\n    tf.keras.layers.RandomFlip(mode=\"horizontal\", seed=42),\n    tf.keras.layers.RandomRotation(factor=0.05, seed=42),\n    tf.keras.layers.RandomContrast(factor=0.2, seed=42)\n])","metadata":{"id":"Ib0cA8Y1pKz9","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Try running the following cell multiple times to see different random data augmentations:","metadata":{"id":"G7GrQjsspKz-"}},{"cell_type":"code","source":"# extra code – displays the same first 9 images, after augmentation\n\nplt.figure(figsize=(12, 12))\nfor X_batch, y_batch in valid_set.take(1):\n    X_batch_augmented = data_augmentation(X_batch, training=True)\n    for index in range(9):\n        plt.subplot(3, 3, index + 1)\n        # We must rescale the images to the 0-1 range for imshow(), and also\n        # clip the result to that range, because data augmentation may\n        # make some values go out of bounds (e.g., RandomContrast in this case).\n        plt.imshow(np.clip((X_batch_augmented[index] + 1) / 2, 0, 1))\n        if(y_batch[index]==1):\n            classt='FAKE'\n        else:\n            classt='REAL'\n        plt.title(f\"Class: {classt}\")\n        plt.axis(\"off\")\n\nplt.show()","metadata":{"id":"w6GH5_vupKz-","outputId":"eeb2c924-2f4f-4aa1-bea9-951bebef4bf0","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Now let's load the pretrained model, without its top layers, and replace them with our own task","metadata":{"id":"kNL9AOsDpKz-"}},{"cell_type":"code","source":"tf.random.set_seed(42)  # extra code – ensures reproducibility\nbase_model = tf.keras.applications.xception.Xception(weights=\"imagenet\",\n                                                     include_top=False)\navg = tf.keras.layers.GlobalAveragePooling2D()(base_model.output)\noutput = tf.keras.layers.Dense(1, activation=\"sigmoid\")(avg)\nmodel = tf.keras.Model(inputs=base_model.input, outputs=output)","metadata":{"id":"lRyCgvaKpKz-","outputId":"a825e173-8b1d-4217-a1c4-5491b49c3e82","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for layer in base_model.layers:\n    layer.trainable = False","metadata":{"id":"KBlyG6ElpKz-","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let's train the model for a few epochs, while keeping the base model weights fixed:","metadata":{"id":"WFEFw7GKpKz-"}},{"cell_type":"code","source":"optimizer = tf.keras.optimizers.SGD(learning_rate=0.1, momentum=0.9)\nmodel.compile(loss=\"binary_crossentropy\", optimizer=optimizer,\n              metrics=[\"accuracy\"])\nhistory = model.fit(train_set, validation_data=valid_set, epochs=3)","metadata":{"id":"GGxK2yPcpKz-","outputId":"6b64214a-e104-4b6c-9b7a-3388fc9aa15f","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for indices in zip(range(33), range(33, 66), range(66, 99), range(99, 132)):\n    for idx in indices:\n        print(f\"{idx:3}: {base_model.layers[idx].name:22}\", end=\"\")\n    print()","metadata":{"id":"GvGMiJMLpKz-","outputId":"91f2c96c-c058-45e0-e428-66fa6076ad56","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.evaluate(test_set)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.save('my_model2.h5')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Now with the finetuning the top layers of xception model the model performance jumps to 63.8% ","metadata":{}},{"cell_type":"markdown","source":"Now that the weights of our new top layers are not too bad, we can make the top part of the base model trainable again, and continue training, but with a lower learning rate:","metadata":{"id":"L_bEwL8KpKz_"}},{"cell_type":"code","source":"for layer in base_model.layers[56:]:\n    layer.trainable = True\n\noptimizer = tf.keras.optimizers.SGD(learning_rate=0.01, momentum=0.9)\nmodel.compile(loss=\"binary_crossentropy\", optimizer=optimizer,\n              metrics=[\"accuracy\"])\nhistory = model.fit(train_set, validation_data=valid_set, epochs=3)","metadata":{"id":"GEUNGlhvpKz_","outputId":"c622a91d-f634-4443-b87e-8d46defdb578","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.save('xception_deepfake_image.h5')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# plot model performance\nacc = history.history['accuracy']\nval_acc = history.history['val_accuracy']\nloss = history.history['loss']\nval_loss = history.history['val_loss']\nepochs_range = range(1, len(history.epoch) + 1)\n\nplt.figure(figsize=(15,5))\n\nplt.subplot(1, 2, 1)\nplt.plot(epochs_range, acc, label='Train Set')\nplt.plot(epochs_range, val_acc, label='Val Set')\nplt.legend(loc=\"best\")\nplt.xlabel('Epochs')\nplt.ylabel('Accuracy')\nplt.title('Model Accuracy')\n\nplt.subplot(1, 2, 2)\nplt.plot(epochs_range, loss, label='Train Set')\nplt.plot(epochs_range, val_loss, label='Val Set')\nplt.legend(loc=\"best\")\nplt.xlabel('Epochs')\nplt.ylabel('Loss')\nplt.title('Model Loss')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.evaluate(test_set)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Evaluate on the test set\ny_pred_probs = model.predict(test_set)\ny_true_list = [y.numpy() for _, y in test_set_raw]  # Convert TensorFlow tensors to NumPy arrays\n\n# Use np.hstack instead of np.concatenate\ny_true = np.hstack(y_true_list)\n\n# Convert probabilities to binary predictions using a threshold (e.g., 0.5)\ny_pred_binary = (y_pred_probs > 0.5).astype(int)\n\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import classification_report\n\n# Assuming you have y_true and y_pred_binary from your previous code\n\n# Compute confusion matrix\nconf_matrix = confusion_matrix(y_true, y_pred_binary)\n\n# Display confusion matrix as a heatmap\nplt.figure(figsize=(8, 6))\nsns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['Negative', 'Positive'], yticklabels=['Negative', 'Positive'])\nplt.title('Confusion Matrix')\nplt.xlabel('Predicted')\nplt.ylabel('True')\nplt.show()\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#ROC Curve\nfpr, tpr, thresholds = roc_curve(y_true, y_pred_probs)\nroc_auc = auc(fpr, tpr)\n\n# Plot ROC curve\nimport matplotlib.pyplot as plt\n\nplt.figure(figsize=(8, 6))\nplt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (AUC = {roc_auc:.2f})')\nplt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\nplt.xlabel('False Positive Rate (FPR)')\nplt.ylabel('True Positive Rate (TPR)')\nplt.title('Receiver Operating Characteristic (ROC) Curve')\nplt.legend(loc='lower right')\nplt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The model accuracy finally reaches to 81.9%","metadata":{}},{"cell_type":"markdown","source":"Lets try to interpret the trained model on how it finds a image FAKE","metadata":{}},{"cell_type":"code","source":"!pip install lime","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from lime import lime_image\n\nexplainer = lime_image.LimeImageExplainer()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Deep Fake Video Classification","metadata":{}},{"cell_type":"markdown","source":"## Importing required libraries","metadata":{}},{"cell_type":"code","source":"!pip install -U --upgrade tensorflow","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pip install tensorflow-docs\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from tensorflow_docs.vis import embed\nfrom tensorflow import keras\n#from imutils import paths\nfrom tensorflow_docs.vis import embed\n\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nimport pandas as pd\nimport numpy as np\nimport imageio\nimport cv2\nimport os","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Data Visualisation","metadata":{}},{"cell_type":"code","source":"# prints the number of train and test samples available in the specified folders\n# folder containing the dataset\nDATA_FOLDER = '../input/deepfake-detection-challenge'\nTRAIN_SAMPLE_FOLDER = 'train_sample_videos'\nTEST_FOLDER = 'test_videos'\n\nprint(f\"Train samples: {len(os.listdir(os.path.join(DATA_FOLDER, TRAIN_SAMPLE_FOLDER)))}\")\nprint(f\"Test samples: {len(os.listdir(os.path.join(DATA_FOLDER, TEST_FOLDER)))}\")","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_sample_metadata = pd.read_json('../input/deepfake-detection-challenge/train_sample_videos/metadata.json').T\ntrain_sample_metadata.head()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Groups the metadata DataFrame (train_sample_metadata) by the 'label' column and counts the occurrences of each label.\ntrain_sample_metadata.groupby('label')['label'].count().plot(figsize=(15, 5), kind='bar', title='Distribution of Labels in the Training Set')\nplt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_sample_metadata.shape","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let's visualize now the data.\n\nWe select first a list of fake videos.","metadata":{}},{"cell_type":"markdown","source":"### Few fake videos","metadata":{}},{"cell_type":"code","source":"fake_train_sample_video = list(train_sample_metadata.loc[train_sample_metadata.label=='FAKE'].sample(3).index)\nfake_train_sample_video","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def display_image_from_video(video_path):\n    '''\n    input: video_path - path for video\n    process:\n    1. perform a video capture from the video\n    2. read the image\n    3. display the image\n    '''\n#     Creates a video capture object from the video file located at video_path.\n    capture_image = cv2.VideoCapture(video_path) \n#     capture_image.read(): Reads the first frame from the video capture object.\n#  return value (boolean indicating success) and the frame image.\n    ret, frame = capture_image.read()\n#     a Matplotlib figure with a size of 10x10 inches.\n    fig = plt.figure(figsize=(10,10))\n#     Adds a subplot to the figure.\n    ax = fig.add_subplot(111)\n#     Converts the frame from BGR to RGB color space (OpenCV reads images in BGR format).\n    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n#     Displays the frame image on the subplot. \n    ax.imshow(frame)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for video_file in fake_train_sample_video:\n    display_image_from_video(os.path.join(DATA_FOLDER, TRAIN_SAMPLE_FOLDER, video_file))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let's try now the same for few of the images that are real.","metadata":{}},{"cell_type":"markdown","source":"### Few Real Videos","metadata":{}},{"cell_type":"code","source":"real_train_sample_video = list(train_sample_metadata.loc[train_sample_metadata.label=='REAL'].sample(3).index)\nreal_train_sample_video","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for video_file in real_train_sample_video:\n    display_image_from_video(os.path.join(DATA_FOLDER, TRAIN_SAMPLE_FOLDER, video_file))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Videos with same original","metadata":{}},{"cell_type":"markdown","source":"Let's look now to set of samples with the same original.","metadata":{}},{"cell_type":"code","source":"train_sample_metadata['original'].value_counts()[0:5]","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We pick one of the originals with largest number of samples.\n\nWe also modify our visualization function to work with multiple images.","metadata":{}},{"cell_type":"code","source":"def display_image_from_video_list(video_path_list, video_folder=TRAIN_SAMPLE_FOLDER):\n    '''\n    input: video_path_list - path for video\n    process:\n    0. for each video in the video path list\n        1. perform a video capture from the video\n        2. read the image\n        3. display the image\n    '''\n    plt.figure()\n#     Creates a 2x3 grid of subplots (total 6 subplots) with a specified size of 16x8 inches.\n    fig, ax = plt.subplots(2,3,figsize=(16,8))\n    # we only show images extracted from the first 6 videos\n#     Iterates over the first 6 video filenames in the video_path_list.\n    for i, video_file in enumerate(video_path_list[0:6]):\n        video_path = os.path.join(DATA_FOLDER, video_folder,video_file)\n#         Creates a video capture object from the video file.\n        capture_image = cv2.VideoCapture(video_path) \n#     Reads the first frame from the video capture object\n        ret, frame = capture_image.read()\n#     Converts the frame from BGR to RGB color space.\n        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n#     Displays the frame image on the appropriate subplot.\n        ax[i//3, i%3].imshow(frame)\n        ax[i//3, i%3].set_title(f\"Video: {video_file}\")\n#          Turns on the axis for the subplot.\n        ax[i//3, i%3].axis('on')\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"same_original_fake_train_sample_video = list(train_sample_metadata.loc[train_sample_metadata.original=='atvmxvwyns.mp4'].index)\ndisplay_image_from_video_list(same_original_fake_train_sample_video)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Test video files","metadata":{}},{"cell_type":"markdown","source":"Let's also look to few of the test data files.","metadata":{}},{"cell_type":"code","source":"test_videos = pd.DataFrame(list(os.listdir(os.path.join(DATA_FOLDER, TEST_FOLDER))), columns=['video'])","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_videos.head()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let's visualize now one of the videos.","metadata":{}},{"cell_type":"code","source":"display_image_from_video(os.path.join(DATA_FOLDER, TEST_FOLDER, test_videos.iloc[2].video))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Play video files","metadata":{}},{"cell_type":"markdown","source":"Let's look to few fake videos.","metadata":{}},{"cell_type":"code","source":"fake_videos = list(train_sample_metadata.loc[train_sample_metadata.label=='FAKE'].index)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from IPython.display import HTML\nfrom base64 import b64encode\n\ndef play_video(video_file, subset=TRAIN_SAMPLE_FOLDER):\n    '''\n    Display video\n    param: video_file - the name of the video file to display\n    param: subset - the folder where the video file is located (can be TRAIN_SAMPLE_FOLDER or TEST_Folder)\n    '''\n    video_url = open(os.path.join(DATA_FOLDER, subset,video_file),'rb').read()\n    data_url = \"data:video/mp4;base64,\" + b64encode(video_url).decode()\n    return HTML(\"\"\"<video width=500 controls><source src=\"%s\" type=\"video/mp4\"></video>\"\"\" % data_url)\n\nplay_video(fake_videos[10])","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"From visual inspection of these fakes videos, in some cases is very easy to spot the anomalies created when engineering the deep fake, in some cases is more difficult.","metadata":{}},{"cell_type":"markdown","source":"## Modelling","metadata":{}},{"cell_type":"markdown","source":"### A CNN-RNN Architecture","metadata":{}},{"cell_type":"code","source":"# images will be resized to 224x224 pixels\nIMG_SIZE = 224\n# The number of training examples utilized in one iteration\n# defines how many samples are processed before the model's parameters are updated.\nBATCH_SIZE = 64\nEPOCHS = 10\n\nMAX_SEQ_LENGTH = 20\nNUM_FEATURES = 2048","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":" In this example we will do the following:\n\n* Capture the frames of a video.\n* Extract frames from the videos until a maximum frame count is reached.\n* In the case, where a video's frame count is lesser than the maximum frame count we will pad the video with zeros.","metadata":{}},{"cell_type":"code","source":"def crop_center_square(frame):\n    y, x = frame.shape[0:2]\n    min_dim = min(y, x)\n    start_x = (x // 2) - (min_dim // 2)\n    start_y = (y // 2) - (min_dim // 2)\n    return frame[start_y : start_y + min_dim, start_x : start_x + min_dim]\n\n\ndef load_video(path, max_frames=0, resize=(IMG_SIZE, IMG_SIZE)):\n    cap = cv2.VideoCapture(path)\n    frames = []\n    try:\n        while True:\n            ret, frame = cap.read()\n            if not ret:\n                break\n            frame = crop_center_square(frame)\n            frame = cv2.resize(frame, resize)\n            frame = frame[:, :, [2, 1, 0]]\n            frames.append(frame)\n\n            if len(frames) == max_frames:\n                break\n    finally:\n        cap.release()\n    return np.array(frames)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We can use a pre-trained network to extract meaningful features from the extracted frames. The Keras Applications module provides a number of state-of-the-art models pre-trained on the ImageNet-1k dataset. We will be using the InceptionV3 model for this purpose.","metadata":{}},{"cell_type":"code","source":"def build_feature_extractor():\n    feature_extractor = keras.applications.InceptionV3(\n        weights=\"imagenet\",\n        include_top=False,\n        pooling=\"avg\",\n        input_shape=(IMG_SIZE, IMG_SIZE, 3),\n    )\n    preprocess_input = keras.applications.inception_v3.preprocess_input\n\n    inputs = keras.Input((IMG_SIZE, IMG_SIZE, 3))\n    preprocessed = preprocess_input(inputs)\n\n    outputs = feature_extractor(preprocessed)\n    return keras.Model(inputs, outputs, name=\"feature_extractor\")\n\n\nfeature_extractor = build_feature_extractor()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Finally, we can put all the pieces together to create our data processing utility.","metadata":{}},{"cell_type":"code","source":"def prepare_all_videos(df, root_dir):\n    num_samples = len(df)\n    video_paths = list(df.index)\n    labels = df[\"label\"].values\n    labels = np.array(labels == 'FAKE').astype(int)\n\n\n    # `frame_masks` and `frame_features` are what we will feed to our sequence model.\n    # `frame_masks` will contain a bunch of booleans denoting if a timestep is\n    # masked with padding or not.\n    frame_masks = np.zeros(shape=(num_samples, MAX_SEQ_LENGTH), dtype=\"bool\")\n    frame_features = np.zeros(\n        shape=(num_samples, MAX_SEQ_LENGTH, NUM_FEATURES), dtype=\"float32\"\n    )\n\n    # For each video.\n    for idx, path in enumerate(video_paths):\n        # Gather all its frames and add a batch dimension.\n        frames = load_video(os.path.join(root_dir, path))\n        frames = frames[None, ...]\n\n        # Initialize placeholders to store the masks and features of the current video.\n        temp_frame_mask = np.zeros(shape=(1, MAX_SEQ_LENGTH,), dtype=\"bool\")\n        temp_frame_features = np.zeros(\n            shape=(1, MAX_SEQ_LENGTH, NUM_FEATURES), dtype=\"float32\"\n        )\n\n        # Extract features from the frames of the current video.\n        for i, batch in enumerate(frames):\n            video_length = batch.shape[0]\n            length = min(MAX_SEQ_LENGTH, video_length)\n            for j in range(length):\n                temp_frame_features[i, j, :] = feature_extractor.predict(\n                    batch[None, j, :]\n                )\n            temp_frame_mask[i, :length] = 1  # 1 = not masked, 0 = masked\n\n        frame_features[idx,] = temp_frame_features.squeeze()\n        frame_masks[idx,] = temp_frame_mask.squeeze()\n\n    return (frame_features, frame_masks), labels","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Since we don't have test labels we split the training data to find its performance in unseen data","metadata":{}},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\n\nTrain_set, Test_set = train_test_split(train_sample_metadata,test_size=0.1,random_state=42,stratify=train_sample_metadata['label'])\n\nprint(Train_set.shape, Test_set.shape )","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_data, train_labels = prepare_all_videos(Train_set, \"train\")\ntest_data, test_labels = prepare_all_videos(Test_set, \"test\")\n\n# Assuming train_data is a tuple with two elements: (frame_features, frame_masks)\nframe_features_shape = train_data[0].shape if train_data and len(train_data) > 0 else None\nframe_masks_shape = train_data[1].shape if train_data and len(train_data) > 1 else None\n\nprint(f\"Frame features in train set: {frame_features_shape}\")\nprint(f\"Frame masks in train set: {frame_masks_shape}\")\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## The sequence model","metadata":{}},{"cell_type":"markdown","source":"Now, we can feed this data to a sequence model consisting of recurrent layers like GRU.","metadata":{}},{"cell_type":"code","source":"frame_features_input = keras.Input((MAX_SEQ_LENGTH, NUM_FEATURES))\nmask_input = keras.Input((MAX_SEQ_LENGTH,), dtype=\"bool\")\n\n# Refer to the following tutorial to understand the significance of using `mask`:\n# https://keras.io/api/layers/recurrent_layers/gru/\nx = keras.layers.GRU(16, return_sequences=True)(\n    frame_features_input, mask=mask_input\n)\nx = keras.layers.GRU(8)(x)\nx = keras.layers.Dropout(0.4)(x)\nx = keras.layers.Dense(8, activation=\"relu\")(x)\noutput = keras.layers.Dense(1, activation=\"sigmoid\")(x)\n\nmodel = keras.Model([frame_features_input, mask_input], output)\n\nmodel.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\nmodel.summary()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pip install --upgrade tensorflow\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"checkpoint = keras.callbacks.ModelCheckpoint('./', save_weights_only=True, save_best_only=True)\nhistory = model.fit(\n        [train_data[0], train_data[1]],\n        train_labels,\n        validation_data=([test_data[0], test_data[1]],test_labels),\n        callbacks=[checkpoint],\n        epochs=EPOCHS,\n        batch_size=8\n    )","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Inference","metadata":{}},{"cell_type":"code","source":"def prepare_single_video(frames):\n    frames = frames[None, ...]\n    frame_mask = np.zeros(shape=(1, MAX_SEQ_LENGTH,), dtype=\"bool\")\n    frame_features = np.zeros(shape=(1, MAX_SEQ_LENGTH, NUM_FEATURES), dtype=\"float32\")\n\n    for i, batch in enumerate(frames):\n        video_length = batch.shape[0]\n        length = min(MAX_SEQ_LENGTH, video_length)\n        for j in range(length):\n            frame_features[i, j, :] = feature_extractor.predict(batch[None, j, :])\n        frame_mask[i, :length] = 1  # 1 = not masked, 0 = masked\n\n    return frame_features, frame_mask\n\ndef sequence_prediction(path):\n    frames = load_video(os.path.join(DATA_FOLDER, TEST_FOLDER,path))\n    frame_features, frame_mask = prepare_single_video(frames)\n    return model.predict([frame_features, frame_mask])[0]\n    \n\ndef to_gif(images):\n    converted_images = images.astype(np.uint8)\n    imageio.mimsave(\"animation.gif\", converted_images, fps=10)\n    return embed.embed_file(\"animation.gif\")\n\n\ntest_video = input(\"Enter the path of the video: \")\nprint(f\"Test video path: {test_video}\")\n\nif(sequence_prediction(test_video)>=0.5):\n    print(f'The predicted class of the video is FAKE')\nelse:\n    print(f'The predicted class of the video is REAL')\n\nplay_video(test_video,TEST_FOLDER)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import os\nimport numpy as np\nimport imageio\nfrom tensorflow.keras.models import load_model\n\n# Assuming you have defined MAX_SEQ_LENGTH, NUM_FEATURES, DATA_FOLDER, TEST_FOLDER, and feature_extractor\n\ndef prepare_single_video(frames):\n    frames = frames[None, ...]\n    frame_mask = np.zeros(shape=(1, MAX_SEQ_LENGTH,), dtype=\"bool\")\n    frame_features = np.zeros(shape=(1, MAX_SEQ_LENGTH, NUM_FEATURES), dtype=\"float32\")\n\n    for i, batch in enumerate(frames):\n        video_length = batch.shape[0]\n        length = min(MAX_SEQ_LENGTH, video_length)\n        for j in range(length):\n            frame_features[i, j, :] = feature_extractor.predict(batch[None, j, :])\n        frame_mask[i, :length] = 1  # 1 = not masked, 0 = masked\n\n    return frame_features, frame_mask\n\ndef sequence_prediction(path):\n    frames = load_video(os.path.join(DATA_FOLDER, TEST_FOLDER, path))\n    frame_features, frame_mask = prepare_single_video(frames)\n    return model.predict([frame_features, frame_mask])[0]\n\ndef visualize_frames_with_predictions(video_path):\n    frames = load_video(os.path.join(DATA_FOLDER, TEST_FOLDER, video_path))\n\n    for i, frame in enumerate(frames):\n        frame_features, frame_mask = prepare_single_video(np.array([frame]))\n        prediction = sequence_prediction(video_path)\n\n        # Print or save the individual frame along with its prediction\n        print(f\"Frame {i}: {'FAKE' if prediction >= 0.5 else 'REAL'}\")\n        plt.imshow(frame)  # Assuming you have matplotlib for visualization\n        plt.show()\n\ntest_video = input(\"Enter the path of the video: \")\nprint(f\"Test video path: {test_video}\")\n\n# Visualize frames with predictions\nvisualize_frames_with_predictions(test_video)\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]}]}