ebook/ebook_imagenet_jsma_tf.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "介绍如何在tensorflow环境下，使用JSMA算法攻击基于Inception数据集预训练的alexnet模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jupyter notebook中使用Anaconda中的环境需要单独配置，默认情况下使用的是系统默认的Python环境，以使用advbox环境为例。\n",
    "首先在默认系统环境下执行以下命令，安装ipykernel。\n",
    "\n",
    "    conda install ipykernel\n",
    "    conda install -n advbox ipykernel\n",
    "\n",
    "在advbox环境下激活，这样启动后就可以在界面上看到advbox了。\n",
    "\n",
    "    python -m ipykernel install --user --name advbox --display-name advbox \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "logging.basicConfig(level=logging.INFO,format=\"%(filename)s[line:%(lineno)d] %(levelname)s %(message)s\")\n",
    "logger=logging.getLogger(__name__)\n",
    "\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "#pip install Pillow\n",
    "from adversarialbox.adversary import Adversary\n",
    "from adversarialbox.attacks.saliency import JSMA\n",
    "from adversarialbox.models.tensorflow import TensorflowModel\n",
    "import tensorflow as tf\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "from tools import show_images_diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义被攻击的图片\n",
    "imagename=\"tutorials/cropped_panda.jpg\"\n",
    "#从'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'下载并解压到当前路径\n",
    "dirname=\"classify_image_graph_def.pb\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "tensorflow.py[line:63] INFO self._input_shape:(Dimension(None), Dimension(None), Dimension(3))\n",
      "tensorflow.py[line:65] INFO init grads[1008]...\n",
      "tensorflow.py[line:72] INFO Finish TensorflowPBModel init\n",
      "base.py[line:87] INFO adversary:\n",
      "         original_label: 169\n",
      "         target_label: 651\n",
      "         is_targeted_attack: True\n",
      "saliency.py[line:87] INFO step = 0, original_label = 169, adv_label=169 logit=0.797313213348\n",
      "tensorflow.py[line:127] INFO Start to get _grads[651]\n",
      "tensorflow.py[line:129] INFO Finish to get _grads[651]\n",
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    },
    {
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "attack success, adversarial_label=651\n",
      "jsma attack done\n"
     ]
    }
   ],
   "source": [
    "#加载解码的图像 这里是个大坑 tf提供的imagenet预训练好的模型pb文件中 包含针对图像的预处理环节 即解码jpg文件 这部分没有梯度\n",
    "#需要直接处理解码后的数据\n",
    "image=np.array(Image.open(imagename).convert('RGB').resize((224,224))).astype(np.float32)\n",
    "#[100,100,3]->[1,100,100,3]\n",
    "\n",
    "orig=image.copy().astype(np.uint8) \n",
    "\n",
    "image=np.expand_dims(image, axis=0)\n",
    "\n",
    "  \n",
    "\n",
    "session=tf.Session()\n",
    "\n",
    "def create_graph(dirname):\n",
    "    with tf.gfile.FastGFile(dirname, 'rb') as f:\n",
    "        graph_def = session.graph_def\n",
    "        graph_def.ParseFromString(f.read())\n",
    "\n",
    "        _ = tf.import_graph_def(graph_def, name='')\n",
    "\n",
    "create_graph(dirname)\n",
    "\n",
    "# 初始化参数  非常重要\n",
    "session.run(tf.global_variables_initializer())\n",
    "\n",
    "tensorlist=[n.name for n in session.graph_def.node]\n",
    "\n",
    "#输出全部tensor\n",
    "#logger.info(tensorlist)\n",
    "\n",
    "\n",
    "#获取logits\n",
    "logits=session.graph.get_tensor_by_name('softmax/logits:0')\n",
    "\n",
    "x = session.graph.get_tensor_by_name('ExpandDims:0')\n",
    "\n",
    "# advbox demo\n",
    "# 因为原始数据没有归一化  所以bounds=(0, 255)\n",
    "m = TensorflowModel(\n",
    "    session,\n",
    "    x,\n",
    "    None,\n",
    "    logits,\n",
    "    None,\n",
    "    bounds=(0, 255),\n",
    "    channel_axis=3,\n",
    "    preprocess=None)\n",
    "\n",
    "#实例化JSMA max_iter为最大迭代次数  theta为扰动系数 max_perturbations_per_pixel为单像素最大修改次数\n",
    "attack = JSMA(m)\n",
    "attack_config = {\n",
    "        \"max_iter\": 2000,\n",
    "        \"theta\": 0.3,\n",
    "        \"max_perturbations_per_pixel\": 7,\n",
    "        \"fast\":True,\n",
    "        \"two_pix\":False\n",
    "}\n",
    "\n",
    "\n",
    "adversary = Adversary(image,None)\n",
    "#麦克风\n",
    "tlabel = 651\n",
    "adversary.set_target(is_targeted_attack=True, target_label=tlabel)\n",
    "\n",
    "# FGSM targeted attack\n",
    "adversary = attack(adversary, **attack_config)\n",
    "\n",
    "if adversary.is_successful():\n",
    "    print(\n",
    "        'attack success, adversarial_label=%d'\n",
    "        % (adversary.adversarial_label) )\n",
    "\n",
    "    #对抗样本保存在adversary.adversarial_example\n",
    "    adversary_image=np.copy(adversary.adversarial_example)\n",
    "    #强制类型转换 之前是float 现在要转换成int8\n",
    "\n",
    "    adv = np.array(adversary_image).astype(\"uint8\")[0]\n",
    "\n",
    " \n",
    "\n",
    "print(\"jsma attack done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "l0=1811 l2=3296.16443765\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#显示原始图片  抵抗样本 以及两张图之间的差异  其中灰色代表没有差异的像素点\n",
    "show_images_diff(orig,adversary.original_label,adv,adversary.adversarial_label)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "advbox",
   "language": "python",
   "name": "advbox"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}