{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "55QXGd7CFEjt", "outputId": "a36e22e3-9916-4008-dea5-2d548fddd028" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading...\n", "From: https://drive.google.com/uc?id=15QrQ8vXDqIBAB0uQs41q3E0aGKaFicfm\n", "To: /content/Au.zip\n", "100% 341M/341M [00:01<00:00, 302MB/s]\n", "Downloading...\n", "From: https://drive.google.com/uc?id=1w9gWCk0DeGOVGOKqgTNkbVx8WAPRL8Fp\n", "To: /content/CASIA 2 Groundtruth.zip\n", "100% 4.82M/4.82M [00:00<00:00, 295MB/s]\n", "Downloading...\n", "From: https://drive.google.com/uc?id=1QJFJMw5-T2tU6Wi-Hqj9uR1AT8EDW0s6\n", "To: /content/Tp.zip\n", "100% 2.40G/2.40G [00:09<00:00, 244MB/s]\n" ] } ], "source": [ "!gdown 15QrQ8vXDqIBAB0uQs41q3E0aGKaFicfm\n", "!gdown 1w9gWCk0DeGOVGOKqgTNkbVx8WAPRL8Fp\n", "!gdown 1QJFJMw5-T2tU6Wi-Hqj9uR1AT8EDW0s6" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Wk9tBSh5FVK4", "outputId": "c986dc3f-a971-4f48-e45e-6ee0beb9acf5" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n", " inflating: CASIA 2 Groundtruth/Tp_D_CRN_M_N_art00067_nat00059_11805_gt.png \n", " inflating: CASIA 2 Groundtruth/Tp_D_CRN_M_N_art00067_nat00095_11807_gt.png \n", " inflating: CASIA 2 Groundtruth/Tp_D_CRN_M_N_art00067_nat10122_11806_gt.png \n", " inflating: CASIA 2 Groundtruth/Tp_D_CRN_M_N_art00097_art00049_11758_gt.png \n", " inflating: CASIA 2 Groundtruth/Tp_D_CRN_M_N_art10107_cha10112_11582_gt.png \n", " inflating: CASIA 2 Groundtruth/Tp_D_CRN_M_N_art10112_cha00086_11672_gt.png \n", " inflating: CASIA 2 Groundtruth/Tp_D_CRN_M_N_art10113_cha00067_11650_gt.png \n", " inflating: CASIA 2 Groundtruth/Tp_D_CRN_M_N_art10115_cha00086_11526_gt.png \n", " inflating: CASIA 2 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-d \"\"\n", "!unzip \"Tp.zip\" -d \"\"\n", "!unzip \"CASIA 2 Groundtruth.zip\" -d \"\"" ] }, { "cell_type": "markdown", "metadata": { "id": "K6UVBfhpGBhl" }, "source": [ "Number of images over the internet is increasing at exponential rate with the advancement of internet and availability of camera to the large proportion of population. Use of image processing software like Adobe Photoshop and Gnu Gimp can easily make forgery photo in which another image is copy pasted on original iamge .Doctored images are major concern for internet companies. These images are prime source of fake news and which are often used for mob incitement . With advancement of photo and video editing softwares it is becoming very hard for a normal human being to differentiate between a real image and a forged image we need forensic experts to do it . So we will try to build a network that will tell us which image is real and which is forged plus it will show us region of the image where some new image is copied and pasted ." ] }, { "cell_type": "markdown", "metadata": { "id": "lruMeuvYGEW_" }, "source": [ "### BUSINESS OBJECTIVE AND CONSTRAINT " ] }, { "cell_type": "markdown", "metadata": { "id": "I8l1CWYgGHwW" }, "source": [ "- There is no latency requirement as such because we don't need instant results from our model but us it should not take lot of time as well to detect forgery region and whether image is real or fake" ] }, { "cell_type": "markdown", "metadata": { "id": "-Mu7Ou5PGLed" }, "source": [ "### EXPLORATORY DATA ANALYSIS AND DATA PREPROCESSING " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "FGYvfkv9GLvm" }, "outputs": [], "source": [ "#Importing necessary libraries\n", "import os\n", "import pandas as pd\n", "import numpy as np \n", "import matplotlib.pyplot as plt\n", "import cv2\n", "from tqdm import tqdm\n", "import pickle as pkl\n", "from keras.utils.vis_utils import plot_model\n", "import keras\n", "from keras.utils.np_utils import to_categorical\n", "from keras.models import Sequential\n", "from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D, BatchNormalization\n", "from sklearn.model_selection import train_test_split\n", "from scipy import stats\n", "from sklearn.preprocessing import LabelEncoder\n", "from PIL import Image\n", "from PIL import Image, ImageChops, ImageEnhance\n", "from sklearn.utils import shuffle\n", "import tensorflow as tf" ] }, { "cell_type": "markdown", "metadata": { "id": "FJieVHhVGQHg" }, "source": [ "I downloaded data from this kaggle link https://www.kaggle.com/datasets/divg07/casia-20-image-tampering-detection-dataset in which we have three files
\n", "- Tp - This folder contain fake image \n", "- CASIA 2 Groundtruth - This folder contain fake image mask \n", "- Au - This folder contain real images" ] }, { "cell_type": "markdown", "metadata": { "id": "KGtrfR_YGS-D" }, "source": [ "#### Analysis on fake images " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "3vt1KhNLGNMI" }, "outputs": [], "source": [ "fake_image_data={'image_path':[],'label':[],'image_id':[]}" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "DNwx4E84GUiW" }, "outputs": [], "source": [ "fake_image_path='./Tp'\n", "for img in os.listdir(fake_image_path) :\n", " if img.endswith('jpg') or img.endswith('png') :\n", " temp_path=fake_image_path+\"/\"+str(img)\n", " fake_image_data['image_path'].append(temp_path)\n", " fake_image_data['label'].append('fake')\n", " fake_image_data['image_id'].append(img[:-4])\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "ZxMaFt4aGVxF", "outputId": "12474ad1-8c3a-4c2f-df2a-8274b8f61c12" }, "outputs": [ { "data": { "text/html": [ "\n", "
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image_pathlabelimage_id
0./Tp/Tp_S_NNN_M_N_ind00080_ind00080_10666.jpgfakeTp_S_NNN_M_N_ind00080_ind00080_10666
1./Tp/Tp_D_NRN_M_N_arc00054_nat00013_11932.jpgfakeTp_D_NRN_M_N_arc00054_nat00013_11932
2./Tp/Tp_D_NRN_M_N_nat10145_nat10145_11978.jpgfakeTp_D_NRN_M_N_nat10145_nat10145_11978
3./Tp/Tp_D_NNN_S_N_nat10159_nat10157_12049.jpgfakeTp_D_NNN_S_N_nat10159_nat10157_12049
4./Tp/Tp_D_NRN_M_N_cha00001_cha00062_11377.jpgfakeTp_D_NRN_M_N_cha00001_cha00062_11377
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image_pathlabelimage_id
0./Au/Au_nat_20083.jpgrealAu_nat_20083
1./Au/Au_arc_00039.jpgrealAu_arc_00039
2./Au/Au_nat_30247.jpgrealAu_nat_30247
3./Au/Au_sec_20044.jpgrealAu_sec_20044
4./Au/Au_pla_30559.jpgrealAu_pla_30559
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\n", " " ], "text/plain": [ " image_path label image_id\n", "0 ./Au/Au_nat_20083.jpg real Au_nat_20083\n", "1 ./Au/Au_arc_00039.jpg real Au_arc_00039\n", "2 ./Au/Au_nat_30247.jpg real Au_nat_30247\n", "3 ./Au/Au_sec_20044.jpg real Au_sec_20044\n", "4 ./Au/Au_pla_30559.jpg real Au_pla_30559" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "real_image_data=pd.DataFrame(real_image_data)\n", "real_image_data.head()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "t7vjQtQeWQC8", "outputId": "a4831554-022e-4c24-c5d7-37e884430342" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of real images are 7437\n" ] } ], "source": [ "print(\"Number of real images are {}\".format(real_image_data.shape[0]))" ] }, { "cell_type": "markdown", "metadata": { "id": "8-w1FDKRWZEe" }, "source": [ "We can see significant difference between number of prisitine images and number of fake images which can lead to problem of imbalance dataset thus i will reduce the number of prisitne images . This technique is called as Undersampling" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "id": "Uj7jLJvrWRvn" }, "outputs": [], "source": [ "real_image_data=shuffle(real_image_data,random_state=42)\n", "real_image_data=real_image_data.iloc[:fake_image_data.shape[0],:]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "id": "ZAhpClrjWb6S" }, "outputs": [], "source": [ "final_image_data=pd.concat([fake_image_data,real_image_data])" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "230DKRZ9Wj4v", "outputId": "5edafd7d-069a-441e-9f10-5d802b215e55" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total number of images in dataset is 4128\n" ] } ], "source": [ "print(\"Total number of images in dataset is {}\".format(final_image_data.shape[0]))" ] }, { "cell_type": "markdown", "metadata": { "id": "a4sfs3WbWod5" }, "source": [ "##### IMAGE PREPROCESSING (ERROR LEVEL ANALYSIS)" ] }, { "cell_type": "markdown", "metadata": { "id": "vRKdUGRgWohg" }, "source": [ "- Research Paper - https://www.researchgate.net/publication/332561655_Image_forgery_detection_using_error_level_analysis_and_deep_learning\n", "- Reference https://www.kaggle.com/code/bchirag/casia-version2" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "id": "mkTsixjhWloz" }, "outputs": [], "source": [ "def convert_to_ela_image(path, quality):\n", " temp_filename = 'temp_file.jpg'\n", " ela_filename = 'temp_ela_file.png'\n", " \n", " image = Image.open(path).convert('RGB')\n", " image.save(temp_filename, 'JPEG', quality = quality)\n", " temp_image = Image.open(temp_filename)\n", " \n", " ela_image = ImageChops.difference(image, temp_image)\n", " \n", " extrema = ela_image.getextrema()\n", " max_diff = max([ex[1] for ex in extrema])\n", " if max_diff == 0:\n", " max_diff = 1\n", " scale = 255.0 / max_diff\n", " \n", " ela_image = ImageEnhance.Brightness(ela_image).enhance(scale)\n", " \n", " return ela_image" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "id": "0AZPBz4XWwEK" }, "outputs": [], "source": [ "image_size = (128, 128)\n", "\n", "def prepare_image(image_path):\n", " return np.array(convert_to_ela_image(image_path, 85).resize(image_size)).flatten() / 255.0" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "id": "zruv6k2HWxvH" }, "outputs": [], "source": [ "X=[]\n", "Y=[]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "GeEKRZqVWzLQ", "outputId": "1ce8ce36-5109-4258-923f-b715c71463cd" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2675it [00:53, 59.35it/s]/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 8 bytes but only got 2. Skipping tag 41487\n", " \" Skipping tag %s\" % (size, len(data), tag)\n", "/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 8 bytes but only got 0. Skipping tag 41988\n", " \" Skipping tag %s\" % (size, len(data), tag)\n", "4128it [01:16, 54.12it/s]\n" ] } ], "source": [ "for index,rows in tqdm(final_image_data.iterrows()) :\n", " temp_ela_image=prepare_image(rows['image_path'])\n", " X.append(temp_ela_image)\n", " if rows['label']=='fake' :\n", " Y.append(0)\n", " else :\n", " Y.append(1)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "id": "KxkgOIceW0ge" }, "outputs": [], "source": [ "X, Y = shuffle(X, Y, random_state=42)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "id": "xlxlE61DXHbb" }, "outputs": [], "source": [ "X = np.array(X)\n", "X = X.reshape(-1, 128, 128, 3)" ] }, { "cell_type": "markdown", "metadata": { "id": "6AWcb6a9XHut" }, "source": [ "TRAINING AND VALIDATION SET" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "id": "o2s-Ahr2XLY-" }, "outputs": [], "source": [ "X_train, X_val, Y_train, Y_val = train_test_split(X, Y, test_size = 0.2, random_state=42)" ] }, { "cell_type": "markdown", "metadata": { "id": "pHFX-a6cXU1B" }, "source": [ "#### CUSTOM DATAGENERATOR" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "id": "0gVCwEZTXNvJ" }, "outputs": [], "source": [ "class Dataset:\n", " def __init__(self, data_x,data_y):\n", " self.x = []\n", " self.y = []\n", " \n", " for img in data_x :\n", " self.x.append(img)\n", " \n", " for lab in data_y :\n", " self.y.append(lab)\n", " \n", "\n", " def __getitem__(self, i):\n", " \n", " return self.x[i],self.y[i]\n", " \n", "\n", " def __len__(self): # your model.fit_gen requires this function\n", " return len(self.x)\n", "\n", " \n", "class Dataloder(tf.keras.utils.Sequence): \n", " def __init__(self, dataset, batch_size=1,shuffle=False):\n", " self.dataset = dataset\n", " self.batch_size = batch_size\n", " self.shuffle=shuffle\n", " self.indexes = np.arange(len(self.dataset.x))\n", "\n", "\n", " def __getitem__(self, i):\n", " start = i * self.batch_size\n", " stop = (i + 1) * self.batch_size\n", " data = []\n", " for j in range(start, stop):\n", " data.append(self.dataset[j])\n", "\n", " #batch = [for samples in zip(*data)]\n", " batch = [np.stack(samples, axis=0) for samples in zip(*data)]\n", "\n", " return tuple([batch[0],batch[1]])\n", "\n", " def __len__(self): # your model.fit_gen requires this function\n", " return len(self.indexes) // self.batch_size\n", " \n", " def on_epoch_end(self):\n", " self.indexes = np.random.permutation(self.indexes)\n", "\n", "train_dataset=Dataset(X_train,Y_train)\n", "train_dataloader = Dataloder(train_dataset, batch_size=8,shuffle=True)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "id": "tGYSWcJdXWW7" }, "outputs": [], "source": [ "test_dataset=Dataset(X_val,Y_val)\n", "test_dataloader = Dataloder(test_dataset, batch_size=8,shuffle=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "XAa8GvT6XZb0" }, "source": [ "#### MODEL BUILDING AND TRAINING" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "id": "XjslCNMFXYOk" }, "outputs": [], "source": [ "from tensorflow.keras.layers import * \n", "from tensorflow.keras.models import *" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "IZtg1BcQXczk", "outputId": "3995e16e-c0aa-4941-dd4b-d9128a4416ec" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"model_1\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " input_2 (InputLayer) [(None, 128, 128, 3)] 0 \n", " \n", " conv2d_2 (Conv2D) (None, 128, 128, 128) 3584 \n", " \n", " conv2d_3 (Conv2D) (None, 128, 128, 64) 73792 \n", " \n", " max_pooling2d_1 (MaxPooling (None, 64, 64, 64) 0 \n", " 2D) \n", " \n", " dropout_1 (Dropout) (None, 64, 64, 64) 0 \n", " \n", " flatten_1 (Flatten) (None, 262144) 0 \n", " \n", " dense_3 (Dense) (None, 16) 4194320 \n", " \n", " dense_4 (Dense) (None, 8) 136 \n", " \n", " dense_5 (Dense) (None, 1) 9 \n", " \n", "=================================================================\n", "Total params: 4,271,841\n", "Trainable params: 4,271,841\n", "Non-trainable params: 0\n", "_________________________________________________________________\n", "None\n" ] } ], "source": [ "Input_shape=Input(shape=(128,128,3))\n", "conv11=Conv2D(filters=128,kernel_size=(3,3),padding=\"same\", activation=\"relu\")(Input_shape)\n", "conv12=Conv2D(filters=64,kernel_size=(3,3),padding=\"same\", activation=\"relu\")(conv11)\n", "conv13=MaxPool2D(pool_size=(2,2),strides=(2,2))(conv12)\n", "conv13=Dropout(0.3)(conv13)\n", "\n", "\n", "flatten1=Flatten()(conv13)\n", "dense1=Dense(units=16,activation=\"relu\")(flatten1)\n", "dense2=Dense(units=8,activation=\"relu\")(dense1)\n", "output=Dense(units=1, activation=\"sigmoid\")(dense2)\n", "\n", "model=Model(inputs=Input_shape,outputs=output)\n", "print(model.summary())\n", "\n" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "id": "4GycUDzrXeG7" }, "outputs": [], "source": [ "model.compile(optimizer =tf.keras.optimizers.Adam(learning_rate=0.0001), loss = 'binary_crossentropy', metrics = ['accuracy'])" ] }, { "cell_type": "markdown", "metadata": { "id": "mNtWQmLuXkX-" }, "source": [ "##### Training the model" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "id": "58IsGCtRMlYr" }, "outputs": [], "source": [ "def scheduler(epoch,lr) :\n", " if epoch%5==0 :\n", " lr=0.9*lr\n", " \n", " return lr" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "id": "1yDL6ihCL6HG" }, "outputs": [], "source": [ "#Callbacks \n", "lrScheduler = tf.keras.callbacks.LearningRateScheduler(scheduler)\n", "es=tf.keras.callbacks.EarlyStopping(monitor=\"val_loss\",patience=3)\n", "rl=tf.keras.callbacks.ReduceLROnPlateau(monitor=\"val_loss\",factor=0.1,patience=5)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "o0Bcdt5g0Oco", "outputId": "ad1d2842-b721-4bfd-d453-dc4017fdbf2e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/30\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n", " \"\"\"Entry point for launching an IPython kernel.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "51/51 [==============================] - 2s 26ms/step - loss: 0.6457 - accuracy: 0.5392 - val_loss: 0.6279 - val_accuracy: 0.4792 - lr: 9.0000e-05\n", "Epoch 2/30\n", "51/51 [==============================] - 1s 23ms/step - loss: 0.5862 - accuracy: 0.5613 - val_loss: 0.5951 - val_accuracy: 0.5729 - lr: 9.0000e-05\n", "Epoch 3/30\n", "51/51 [==============================] - 1s 24ms/step - loss: 0.5534 - accuracy: 0.7402 - val_loss: 0.4903 - val_accuracy: 0.8438 - lr: 9.0000e-05\n", "Epoch 4/30\n", "51/51 [==============================] - 1s 23ms/step - loss: 0.4521 - accuracy: 0.8652 - val_loss: 0.4296 - val_accuracy: 0.8229 - lr: 9.0000e-05\n", "Epoch 5/30\n", "51/51 [==============================] - 1s 24ms/step - loss: 0.4332 - accuracy: 0.8260 - val_loss: 0.3137 - val_accuracy: 0.9271 - lr: 9.0000e-05\n", "Epoch 6/30\n", "51/51 [==============================] - 1s 23ms/step - loss: 0.3461 - accuracy: 0.8701 - val_loss: 0.2687 - val_accuracy: 0.9375 - lr: 8.1000e-05\n", "Epoch 7/30\n", "51/51 [==============================] - 1s 23ms/step - loss: 0.3668 - accuracy: 0.8603 - val_loss: 0.3218 - val_accuracy: 0.8854 - lr: 8.1000e-05\n", "Epoch 8/30\n", "51/51 [==============================] - 1s 24ms/step - loss: 0.3979 - accuracy: 0.8431 - val_loss: 0.2901 - val_accuracy: 0.9167 - lr: 8.1000e-05\n", "Epoch 9/30\n", "51/51 [==============================] - 1s 23ms/step - loss: 0.4144 - accuracy: 0.8284 - val_loss: 0.2957 - val_accuracy: 0.9167 - lr: 8.1000e-05\n" ] } ], "source": [ "history=model.fit_generator(train_dataloader,steps_per_epoch=len(train_dataloader)//8,validation_data=test_dataloader,validation_steps=len(test_dataloader)//8,epochs=30,callbacks=[lrScheduler,es,rl])" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 621 }, "id": "euMdiQX_OdK7", "outputId": "77275ba1-97b6-4fa9-fba7-491e2e5633b6" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,10))\n", "plt.plot(history.history['val_loss'],label='Validation loss')\n", "plt.plot(history.history['loss'],label='Loss')\n", "plt.title(\"Epoch v/s Loss Graph\")\n", "plt.xlabel(\"Epoch\")\n", "plt.ylabel(\"Loss\")\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "id": "H9OA9WoVfVLP" }, "outputs": [], "source": [ "ph1model_json = model.to_json()\n", "with open(\"v1model.json\", \"w\") as json_file:\n", " json_file.write(ph1model_json)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "id": "iRzv35hmfjBt" }, "outputs": [], "source": [ "model.save_weights(\"v1model.h5\")" ] }, { "cell_type": "markdown", "metadata": { "id": "pFSD4P8AbCwU" }, "source": [ "##### PREDICTION" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "id": "N1QZOB5ma8JI" }, "outputs": [], "source": [ "def predict(img_path,model) :\n", " pi=prepare_image(img_path)\n", " pi=pi.reshape(1,128,128,3)\n", " predict=model.predict(pi)\n", " return predict" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "XXh-ESRSbVQ3", "outputId": "0361e77e-571b-4ca9-d17b-6b387b9d7144" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "====================================================================================================\n", "Real label--- real\n", "Predicted label--- real\n", "====================================================================================================\n", "Real label--- real\n", "Predicted label--- real\n", "====================================================================================================\n", "Real label--- real\n", "Predicted label--- real\n", "====================================================================================================\n", "Real label--- fake\n", "Predicted label--- fake\n", "====================================================================================================\n", "Real label--- fake\n", "Predicted label--- fake\n" ] } ], "source": [ "#Prediction on 5 random images \n", "for i in range(1,6) :\n", " ran_num=np.random.randint(0,final_image_data.shape[0])\n", " temp_row=final_image_data.iloc[ran_num,:]\n", " print(\"=\"*100)\n", " temp_arr=plt.imread(temp_row['image_path'])\n", " print(\"Real label--- {}\".format(temp_row['label']))\n", " temp_predict=predict(temp_row['image_path'],model)\n", " if temp_predict[0]>0.5 :\n", " temp_prediction='real'\n", " else:\n", " temp_prediction='fake'\n", " print(\"Predicted label--- {}\".format(temp_prediction))\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "eYAH9NTuLxlq" }, "outputs": [], "source": [] } ], "metadata": { "accelerator": "GPU", "colab": { "name": "ifd PHASE-1.ipynb", "provenance": [] }, "gpuClass": "standard", "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.13" } }, "nbformat": 4, "nbformat_minor": 1 }