Fashion MNIST MIFace Model Inversion Attack.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5ce87d8e",
   "metadata": {},
   "source": [
    "# Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9d615d9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.datasets import fashion_mnist\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from art.estimators.classification import KerasClassifier\n",
    "from art.attacks.inference.model_inversion.mi_face import MIFace\n",
    "tf.compat.v1.disable_eager_execution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebd3f27a",
   "metadata": {},
   "source": [
    "# Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "26770246",
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7105a4a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train.astype('float32') / 255\n",
    "x_test = x_test.astype('float32') / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0c2670ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train.reshape((-1, 28, 28, 1))\n",
    "x_test = x_test.reshape((-1, 28, 28, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b56d256e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = to_categorical(y_train, num_classes=10)\n",
    "y_test = to_categorical(y_test, num_classes=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "daf7c397",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential([\n",
    "    Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=(28, 28, 1)),\n",
    "    MaxPooling2D(pool_size=(2, 2)),\n",
    "    \n",
    "    Conv2D(64, (3, 3), activation='relu', padding='same'),\n",
    "    MaxPooling2D(pool_size=(2, 2)),\n",
    "    \n",
    "    Flatten(),\n",
    "    \n",
    "    Dense(128, activation=\"relu\"),\n",
    "    Dense(10, activation=\"softmax\")\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "eef5bf87",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "63c902f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = KerasClassifier(model=model, clip_values=(0, 1), use_logits=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "853aedb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples\n",
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-08-20 21:00:35.531992: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:982] could not open file to read NUMA node: /sys/bus/pci/devices/0000:08:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-08-20 21:00:35.532479: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1956] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
      "Skipping registering GPU devices...\n",
      "2023-08-20 21:00:35.550185: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:353] MLIR V1 optimization pass is not enabled\n",
      "2023-08-20 21:00:35.584107: W tensorflow/c/c_api.cc:300] Operation '{name:'training/Adam/dense/bias/v/Assign' id:423 op device:{requested: '', assigned: ''} def:{{{node training/Adam/dense/bias/v/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](training/Adam/dense/bias/v, training/Adam/dense/bias/v/Initializer/zeros)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000/60000 [==============================] - 17s 291us/sample - loss: 0.4888 - accuracy: 0.8261\n",
      "Epoch 2/10\n",
      "60000/60000 [==============================] - 17s 289us/sample - loss: 0.3118 - accuracy: 0.8888\n",
      "Epoch 3/10\n",
      "60000/60000 [==============================] - 18s 303us/sample - loss: 0.2672 - accuracy: 0.9030\n",
      "Epoch 4/10\n",
      "60000/60000 [==============================] - 17s 287us/sample - loss: 0.2393 - accuracy: 0.9122\n",
      "Epoch 5/10\n",
      "60000/60000 [==============================] - 17s 288us/sample - loss: 0.2162 - accuracy: 0.9215\n",
      "Epoch 6/10\n",
      "60000/60000 [==============================] - 17s 288us/sample - loss: 0.1939 - accuracy: 0.9289\n",
      "Epoch 7/10\n",
      "60000/60000 [==============================] - 21s 358us/sample - loss: 0.1751 - accuracy: 0.9352\n",
      "Epoch 8/10\n",
      "60000/60000 [==============================] - 19s 310us/sample - loss: 0.1597 - accuracy: 0.9413\n",
      "Epoch 9/10\n",
      "60000/60000 [==============================] - 18s 302us/sample - loss: 0.1437 - accuracy: 0.9474\n",
      "Epoch 10/10\n",
      "60000/60000 [==============================] - 20s 335us/sample - loss: 0.1271 - accuracy: 0.9534\n"
     ]
    }
   ],
   "source": [
    "classifier.fit(x_train, y_train, nb_epochs=10, batch_size=128)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45cd8804",
   "metadata": {},
   "source": [
    "# Attack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "94706039",
   "metadata": {},
   "outputs": [],
   "source": [
    "attack = MIFace(classifier, max_iter=10000, threshold=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "23afc8dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.arange(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "951cb761",
   "metadata": {},
   "outputs": [],
   "source": [
    "classes = {\n",
    "    0: 'T-shirt/top', \n",
    "    1: 'Trouser', \n",
    "    2: 'Pullover', \n",
    "    3: 'Dress', \n",
    "    4: 'Coat', \n",
    "    5: 'Sandal', \n",
    "    6: 'Shirt', \n",
    "    7: 'Sneaker', \n",
    "    8: 'Bag', \n",
    "    9: 'Ankle-boot'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f76df688",
   "metadata": {},
   "outputs": [],
   "source": [
    "avg = np.zeros((10, 28, 28, 1)) + np.mean(x_test, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c6fc776a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7eaaf859512548d3a81cdd8d3ae56e34",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Model inversion:   0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alper/.local/lib/python3.10/site-packages/keras/engine/training_v1.py:2359: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
      "  updates=self.state_updates,\n",
      "2023-08-20 21:27:04.369449: W tensorflow/c/c_api.cc:300] Operation '{name:'dense_1/Softmax' id:102 op device:{requested: '', assigned: ''} def:{{{node dense_1/Softmax}} = Softmax[T=DT_FLOAT, _has_manual_control_dependencies=true](dense_1/BiasAdd)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\n"
     ]
    }
   ],
   "source": [
    "infer_avg = attack.infer(avg, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f8999a83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1000 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "\n",
    "for i in range(5):\n",
    "    ax = plt.subplot(2, 5, i+1)\n",
    "    plt.imshow(x_test[i,], cmap=\"gray\")\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "    \n",
    "    ax = plt.subplot(2, 5, i+1+5)\n",
    "    plt.imshow((np.reshape(infer_avg[i,], (28, 28))), cmap=\"gray\")\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "631f19ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_true = classifier.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1ae146b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRUE:  0.9192\n"
     ]
    }
   ],
   "source": [
    "acc_true = np.sum(np.argmax(pred_true, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)\n",
    "print(\"TRUE: \", acc_true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e7671864",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_adv = classifier.predict(infer_avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d47d00c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "adv:  0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_25569/355926233.py:1: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  acc_adv = np.sum(np.argmax(pred_adv, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)\n"
     ]
    }
   ],
   "source": [
    "acc_adv = np.sum(np.argmax(pred_adv, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)\n",
    "print(\"adv: \", acc_adv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "15474afb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1000 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "\n",
    "for i in range(5):\n",
    "    ax = plt.subplot(2, 5, i+1)\n",
    "    plt.imshow(x_test[i,], cmap=\"gray\")\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "    ax.set_title(f\"Predicted: {classes.get(pred_true[i].argmax())}\", fontsize=20)\n",
    "    \n",
    "    ax = plt.subplot(2, 5, i+1+5)\n",
    "    plt.imshow((np.reshape(infer_avg[i,], (28, 28))), cmap=\"gray\")\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "    ax.set_title(f\"Predicted: {classes.get(pred_adv[i].argmax())}\", fontsize=20)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "972fb835",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}