CAMOU_Subtitute_Model.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "alleged-federal",
      "metadata": {
        "id": "alleged-federal"
      },
      "source": [
        "# CAMOU: Learning Physical Vehicle Camouflages to Adversarially Attack Detectors in the Wild "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "clear-tobago",
      "metadata": {
        "id": "clear-tobago"
      },
      "source": [
        "## Install requirements"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "structured-strike",
      "metadata": {
        "id": "structured-strike"
      },
      "source": [
        "### Install GraphViz and PyDot for Ploting Utils [Run when you don't have it]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "architectural-greeting",
      "metadata": {
        "id": "architectural-greeting",
        "outputId": "a1405f24-2cc3-4277-ebef-8952eca995c4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pydot in /usr/local/lib/python3.8/dist-packages (1.3.0)\n",
            "Requirement already satisfied: pyparsing>=2.1.4 in /usr/local/lib/python3.8/dist-packages (from pydot) (3.0.9)\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "graphviz is already the newest version (2.40.1-2).\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'apt autoremove' to remove it.\n",
            "0 upgraded, 0 newly installed, 0 to remove and 20 not upgraded.\n"
          ]
        }
      ],
      "source": [
        "!pip install pydot\n",
        "!apt install graphviz"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "passive-massachusetts",
      "metadata": {
        "id": "passive-massachusetts"
      },
      "source": [
        "### Enable Tensorflow 2.x for Colab"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "dominican-rubber",
      "metadata": {
        "id": "dominican-rubber",
        "outputId": "a3289664-0999-456a-9a89-86af88ffd7f0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab only includes TensorFlow 2.x; %tensorflow_version has no effect.\n"
          ]
        }
      ],
      "source": [
        "%tensorflow_version 2.x"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "superb-anthony",
      "metadata": {
        "id": "superb-anthony"
      },
      "source": [
        "## Import Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "lightweight-arizona",
      "metadata": {
        "id": "lightweight-arizona"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.utils import plot_model\n",
        "\n",
        "import numpy as np\n",
        "import math"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "joint-winning",
      "metadata": {
        "id": "joint-winning"
      },
      "source": [
        "## Learning A Clone Network Vθ(c,t)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "instructional-associate",
      "metadata": {
        "id": "instructional-associate"
      },
      "source": [
        "![Clone Network](https://github.com/naufalso/camou-iclr2019-tf/blob/main/image-ref/CloneNetwork.png?raw=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "unexpected-variance",
      "metadata": {
        "id": "unexpected-variance"
      },
      "source": [
        "### Define Module"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "incorrect-range",
      "metadata": {
        "id": "incorrect-range"
      },
      "outputs": [],
      "source": [
        "class ConvMax(tf.keras.Model):\n",
        "    def __init__(self, filters=4, kernel_size=3, pool_size=2, activation='relu'):\n",
        "        super(ConvMax, self).__init__(name='ConvMax')\n",
        "\n",
        "        self.conv = tf.keras.layers.Conv2D(filters, kernel_size, padding='same', activation=activation)\n",
        "        self.maxpool = tf.keras.layers.MaxPool2D((pool_size, pool_size))\n",
        "    \n",
        "    def call(self, input_tensor):\n",
        "        x = self.conv(input_tensor)\n",
        "        x = self.maxpool(x)\n",
        "        return x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "tested-madison",
      "metadata": {
        "id": "tested-madison"
      },
      "outputs": [],
      "source": [
        "class RepeatedConvMax(tf.keras.Model):\n",
        "    def __init__(self, repetitions=4, filters=4, kernel_size=3, pool_size=2, activation='relu', **kwargs):\n",
        "        super(RepeatedConvMax, self).__init__(**kwargs)\n",
        "        \n",
        "        self.repetitions = repetitions\n",
        "        self.filters = filters\n",
        "        self.kernel_size = kernel_size\n",
        "        self.pool_size = pool_size\n",
        "        self.activation = activation\n",
        "        \n",
        "        # Define a repeated ConvMax\n",
        "        for i in range(self.repetitions):\n",
        "            # Define a ConvMax layer, specifying filters, kernel_size, pool_size.\n",
        "            setattr(self, f'convMax_{i}', ConvMax(self.filters, self.kernel_size, self.pool_size, self.activation))\n",
        "    \n",
        "    def call(self, input_tensor):\n",
        "        # Connect the first layer\n",
        "        x = getattr(self, 'convMax_0')(input_tensor)\n",
        "       \n",
        "        # Connect the existing layers\n",
        "        for i in range(1, self.repetitions):\n",
        "            x = getattr(self, f'convMax_{i}')(x)\n",
        "        \n",
        "        # return the last layer\n",
        "        return x"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fallen-potato",
      "metadata": {
        "id": "fallen-potato"
      },
      "source": [
        "### Define Object Detection Clone Network"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "cardiac-parking",
      "metadata": {
        "id": "cardiac-parking"
      },
      "outputs": [],
      "source": [
        "class ODCloneNetwork(tf.keras.Model):\n",
        "    def __init__(self, texture_size=64, **kwargs):\n",
        "        super(ODCloneNetwork, self).__init__(**kwargs)\n",
        "        \n",
        "        self.input_texture = tf.keras.layers.Input(shape=(texture_size,texture_size,3,), name=\"texture_input\")\n",
        "        self.input_bg = tf.keras.layers.Input(shape=(64,64,3,), name=\"background_input\")\n",
        "        self.input_fg = tf.keras.layers.Input(shape=(64,64,3,), name=\"foreground_input\")\n",
        "        \n",
        "        self.combined_input = [self.input_texture, self.input_bg, self.input_fg]\n",
        "        \n",
        "        # Define Texture Layers - Texture Repeated ConvMax = log2(texture_size)\n",
        "        texture_repeated_conv_max = int(math.log(texture_size, 2)) - 2\n",
        "        self.texture_layer = RepeatedConvMax(texture_repeated_conv_max, name='texture_layer')\n",
        "        \n",
        "        # Define Background Layers (image_size = 64 x 64, Repeated ConvMax = 4)\n",
        "        self.background_layer = RepeatedConvMax(4, name='background_layer')\n",
        "        \n",
        "        # Define Foreground Layers (image_size = 64 x 64, Repeated ConvMax = 4)\n",
        "        self.foreground_layer = RepeatedConvMax(4, name='foreground_layer')\n",
        "        \n",
        "        # Define last Conv2d\n",
        "        self.last_conv2D_layer = tf.keras.layers.Conv2D(4, 3, activation='relu', name='last_conv2d_layer')\n",
        "        \n",
        "        # Define flatten\n",
        "        self.flatten_layer = tf.keras.layers.Flatten()\n",
        "        \n",
        "        # Define last output sigmoid\n",
        "        self.final_output_layer = tf.keras.layers.Dense(1, activation='sigmoid', name='final_output_layer')\n",
        "        \n",
        "        # Reinitialization to fix the model serialization\n",
        "        self.final_output = self.call(self.combined_input)\n",
        "        super(ODCloneNetwork, self).__init__(inputs=self.combined_input, outputs=self.final_output, **kwargs)\n",
        "        \n",
        "        \n",
        "    def call(self, input_tensors):\n",
        "        input_texture, input_background, input_foreground = input_tensors\n",
        "        \n",
        "        output_texture = self.texture_layer(input_texture)\n",
        "        output_background = self.background_layer(input_background)\n",
        "        output_foreground = self.foreground_layer(input_foreground)\n",
        "        \n",
        "        concat_layer = tf.keras.layers.concatenate([output_texture, output_background, output_foreground])\n",
        "        \n",
        "        x = self.last_conv2D_layer(concat_layer)\n",
        "        x = self.flatten_layer(x)\n",
        "        x = self.final_output_layer(x)\n",
        "        \n",
        "        return x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "limited-crystal",
      "metadata": {
        "id": "limited-crystal",
        "outputId": "21fca904-85fd-4320-d912-47330e80d603",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"ObjectDetectionCloneNetwork\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " texture_input (InputLayer)     [(None, 64, 64, 3)]  0           []                               \n",
            "                                                                                                  \n",
            " background_input (InputLayer)  [(None, 64, 64, 3)]  0           []                               \n",
            "                                                                                                  \n",
            " foreground_input (InputLayer)  [(None, 64, 64, 3)]  0           []                               \n",
            "                                                                                                  \n",
            " texture_layer (RepeatedConvMax  (None, 4, 4, 4)     556         ['texture_input[0][0]']          \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " background_layer (RepeatedConv  (None, 4, 4, 4)     556         ['background_input[0][0]']       \n",
            " Max)                                                                                             \n",
            "                                                                                                  \n",
            " foreground_layer (RepeatedConv  (None, 4, 4, 4)     556         ['foreground_input[0][0]']       \n",
            " Max)                                                                                             \n",
            "                                                                                                  \n",
            " concatenate (Concatenate)      (None, 4, 4, 12)     0           ['texture_layer[0][0]',          \n",
            "                                                                  'background_layer[0][0]',       \n",
            "                                                                  'foreground_layer[0][0]']       \n",
            "                                                                                                  \n",
            " last_conv2d_layer (Conv2D)     (None, 2, 2, 4)      436         ['concatenate[0][0]']            \n",
            "                                                                                                  \n",
            " flatten (Flatten)              (None, 16)           0           ['last_conv2d_layer[0][0]']      \n",
            "                                                                                                  \n",
            " final_output_layer (Dense)     (None, 1)            17          ['flatten[0][0]']                \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 2,121\n",
            "Trainable params: 2,121\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "od_clone_network = ODCloneNetwork(64, name='ObjectDetectionCloneNetwork')\n",
        "od_clone_network.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "atlantic-investing",
      "metadata": {
        "id": "atlantic-investing",
        "outputId": "1d552d27-b7a8-4ce8-808a-92fc1513ec04",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "plot_model(od_clone_network, show_shapes=True, show_layer_names=True, expand_nested=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "gross-theta",
      "metadata": {
        "id": "gross-theta",
        "outputId": "342be469-b7bc-4c05-edf5-ef3eca0e40cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "plot_model(od_clone_network, show_shapes=True, show_layer_names=True, expand_nested=True, rankdir='LR')"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wA526r_WFVX3"
      },
      "id": "wA526r_WFVX3",
      "execution_count": 9,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.6.9"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}