code_practices/Opt_Attack_for_CIFAR_10.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Opt Attack for CIFAR-10",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ndb796/Deep-Learning-Paper-Review-and-Practice/blob/master/code_practices/Opt_Attack_for_CIFAR_10.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OrPgZRyVo_dJ"
      },
      "source": [
        "#### <b>Load Required Libraries</b>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3NN3tJgCjttX",
        "outputId": "56a50523-2e21-4e3c-8c66-4f5277acd84e"
      },
      "source": [
        "!git clone https://github.com/ndb796/attackbox"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'attackbox'...\n",
            "remote: Enumerating objects: 594, done.\u001b[K\n",
            "remote: Counting objects: 100% (19/19), done.\u001b[K\n",
            "remote: Compressing objects: 100% (13/13), done.\u001b[K\n",
            "remote: Total 594 (delta 7), reused 14 (delta 6), pack-reused 575\u001b[K\n",
            "Receiving objects: 100% (594/594), 74.55 MiB | 23.69 MiB/s, done.\n",
            "Resolving deltas: 100% (298/298), done.\n",
            "Checking out files: 100% (191/191), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "orvMQ3aUpNQl"
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.autograd import Variable\n",
        "import torchvision\n",
        "\n",
        "import numpy as np\n",
        "from numpy import linalg as LA\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import os\n",
        "import time\n",
        "import shutil"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8_8c9VyHpYxN"
      },
      "source": [
        "#### <b>Load Dataset</b>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E0pIjQB-pEeA",
        "outputId": "2f13cf11-61e6-4329-a671-b1c7df83511f"
      },
      "source": [
        "def load_model(model, filename):\n",
        "    \"\"\" Load the training model \"\"\"\n",
        "    model.load_state_dict(torch.load(filename))\n",
        "\n",
        "\n",
        "def load_cifar10_data(batch_size=1):\n",
        "    \"\"\" Load MNIST data from torchvision.datasets \n",
        "        input: None\n",
        "        output: minibatches of train and test sets \n",
        "    \"\"\"\n",
        "    # CIFAR10 Dataset\n",
        "    train_dataset = torchvision.datasets.CIFAR10('./data/cifar10-py', download=True, train=True, transform=torchvision.transforms.ToTensor())\n",
        "    test_dataset = torchvision.datasets.CIFAR10('./data/cifar10-py', download=True, train=False, transform=torchvision.transforms.ToTensor())\n",
        "\n",
        "    # Data Loader (Input Pipeline)\n",
        "    train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)\n",
        "    test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)\n",
        "\n",
        "    return train_loader, test_loader, train_dataset, test_dataset\n",
        "\n",
        "\n",
        "test_batch_size = 1\n",
        "train_loader, test_loader, train_dataset, test_dataset = load_cifar10_data(test_batch_size)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qIuRnHrPpO7c"
      },
      "source": [
        "#### <b>Load Pre-trained Classification Model</b>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YKwl6DoNAm94",
        "outputId": "c7c9a601-0000-4bc4-814d-13068beaf704"
      },
      "source": [
        "cfg = {\n",
        "    'VGG11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],\n",
        "    'VGG13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],\n",
        "    'VGG16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],\n",
        "    'VGG19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],\n",
        "}\n",
        "\n",
        "\n",
        "class VGG_plain(nn.Module):\n",
        "    def __init__(self, vgg_name, nclass, img_width=32):\n",
        "        super(VGG_plain, self).__init__()\n",
        "        self.img_width = img_width\n",
        "        self.features = self._make_layers(cfg[vgg_name])\n",
        "        self.classifier = nn.Linear(512, nclass)\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = self.features(x)\n",
        "        out = out.view(out.size(0), -1)\n",
        "        out = self.classifier(out)\n",
        "        return out\n",
        "\n",
        "    def _make_layers(self, cfg):\n",
        "        layers = []\n",
        "        in_channels = 3\n",
        "        width = self.img_width\n",
        "        for x in cfg:\n",
        "            if x == 'M':\n",
        "                layers += [nn.MaxPool2d(kernel_size=2, stride=2)]\n",
        "                width = width // 2\n",
        "            else:\n",
        "                layers += [nn.Conv2d(in_channels, x, kernel_size=3, padding=1),\n",
        "                           nn.BatchNorm2d(x),\n",
        "                           nn.ReLU(inplace=True)]\n",
        "                in_channels = x\n",
        "        layers += [nn.AvgPool2d(kernel_size=width, stride=1)]\n",
        "        return nn.Sequential(*layers)\n",
        "\n",
        "    def predict(self, image):\n",
        "        self.eval()\n",
        "        image = torch.clamp(image,0,1)\n",
        "        image = Variable(image, volatile=True).view(1,3, 32,32)\n",
        "        if torch.cuda.is_available():\n",
        "            image = image.cuda()\n",
        "        output = self(image)[0]\n",
        "        print(output)\n",
        "        _, predict = torch.max(output.data, 1)\n",
        "        return predict[0]\n",
        "    \n",
        "    def predict_batch(self, image):\n",
        "        self.eval()\n",
        "        image = torch.clamp(image,0,1)\n",
        "        image = Variable(image, volatile=True)\n",
        "        if torch.cuda.is_available():\n",
        "            image = image.cuda()\n",
        "        output = self(image)[0]\n",
        "        _, predict = torch.max(output.data, 1)\n",
        "        return predict\n",
        "\n",
        "\n",
        "def vgg16():\n",
        "    return VGG_plain('VGG16', nclass=10)\n",
        "\n",
        "\n",
        "model_dir = 'attackbox/model/cifar10_vgg_plain.pth'\n",
        "net = vgg16()\n",
        "net = torch.nn.DataParallel(net, device_ids=[0])\n",
        "load_model(net, model_dir)\n",
        "net.cuda()\n",
        "net.eval()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DataParallel(\n",
              "  (module): VGG_plain(\n",
              "    (features): Sequential(\n",
              "      (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (2): ReLU(inplace=True)\n",
              "      (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (5): ReLU(inplace=True)\n",
              "      (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "      (7): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (9): ReLU(inplace=True)\n",
              "      (10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (11): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (12): ReLU(inplace=True)\n",
              "      (13): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "      (14): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (16): ReLU(inplace=True)\n",
              "      (17): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (19): ReLU(inplace=True)\n",
              "      (20): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (21): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (22): ReLU(inplace=True)\n",
              "      (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "      (24): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (25): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (26): ReLU(inplace=True)\n",
              "      (27): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (28): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (29): ReLU(inplace=True)\n",
              "      (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (31): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (32): ReLU(inplace=True)\n",
              "      (33): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "      (34): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (35): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (36): ReLU(inplace=True)\n",
              "      (37): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (38): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (39): ReLU(inplace=True)\n",
              "      (40): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (41): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (42): ReLU(inplace=True)\n",
              "      (43): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "      (44): AvgPool2d(kernel_size=1, stride=1, padding=0)\n",
              "    )\n",
              "    (classifier): Linear(in_features=512, out_features=10, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kLnrkPAbp2Ec"
      },
      "source": [
        "class PytorchModel(object):\n",
        "    def __init__(self,model, bounds, num_classes):\n",
        "        self.model = model\n",
        "        self.model.eval()\n",
        "        self.bounds = bounds\n",
        "        self.num_classes = num_classes\n",
        "        self.num_queries = 0\n",
        "\n",
        "    def predict(self,image):\n",
        "        image = torch.clamp(image,self.bounds[0],self.bounds[1]).cuda()\n",
        "        if len(image.size())!=4:\n",
        "            image = image.unsqueeze(0)\n",
        "        output = self.model(image)\n",
        "        self.num_queries += 1\n",
        "        return output\n",
        " \n",
        "    def predict_prob(self,image):\n",
        "        with torch.no_grad():\n",
        "            image = torch.clamp(image,self.bounds[0],self.bounds[1]).cuda()\n",
        "            if len(image.size())!=4:\n",
        "                image = image.unsqueeze(0)\n",
        "            output = self.model(image)\n",
        "            self.num_queries += image.size(0)\n",
        "        return output\n",
        "\n",
        "    def predict_label(self, image, batch=False):\n",
        "        if isinstance(image, np.ndarray):\n",
        "            image = torch.from_numpy(image).type(torch.FloatTensor)\n",
        "        image = torch.clamp(image,self.bounds[0],self.bounds[1]).cuda()\n",
        "        if len(image.size())!=4:\n",
        "            image = image.unsqueeze(0)\n",
        "        with torch.no_grad():\n",
        "            output = self.model(image)\n",
        "            self.num_queries += image.size(0)\n",
        "        _, predict = torch.max(output.data, 1)\n",
        "        if batch:\n",
        "            return predict\n",
        "        else:\n",
        "            return predict[0]\n",
        "\n",
        "    def predict_ensemble(self, image):\n",
        "        if isinstance(image, np.ndarray):\n",
        "            image = torch.from_numpy(image).type(torch.FloatTensor)\n",
        "        image = torch.clamp(image,self.bounds[0],self.bounds[1]).cuda()\n",
        "        if len(image.size())!=4:\n",
        "            image = image.unsqueeze(0)\n",
        "        with torch.no_grad():\n",
        "            output = self.model(image)\n",
        "            output.zero_()\n",
        "            for i in range(10):\n",
        "                output += self.model(image)\n",
        "                self.num_queries += image.size(0)\n",
        "\n",
        "        _, predict = torch.max(output.data, 1)\n",
        "\n",
        "        return predict[0]\n",
        "\n",
        "    def get_num_queries(self):\n",
        "        return self.num_queries\n",
        "\n",
        "    def get_gradient(self,loss):\n",
        "        loss.backward()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D8gtuAehpsSu"
      },
      "source": [
        "#### <b>Adversarial Attack</b>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5-rNUNmBl2mI"
      },
      "source": [
        "MAX_ITER = 1000\n",
        "\n",
        "\n",
        "class OPT_attack(object):\n",
        "    def __init__(self, model):\n",
        "        self.model = model\n",
        "        self.log = torch.ones(MAX_ITER, 2)\n",
        "\n",
        "    def get_log(self):\n",
        "        return self.log\n",
        "\n",
        "    def attack_untargeted(self, x0, y0, alpha=0.2, beta=0.001, iterations=1500, query_limit=20000):\n",
        "        \"\"\" Attack the original image and return adversarial example\n",
        "            model: (pytorch model)\n",
        "            train_dataset: set of training data\n",
        "            (x0, y0): original image\n",
        "        \"\"\"\n",
        "        model = self.model\n",
        "        if type(x0) is torch.Tensor:\n",
        "            x0 = x0.cpu().numpy()\n",
        "        if type(y0) is torch.Tensor:\n",
        "            y0 = y0.item()\n",
        "        if (model.predict_label(x0) != y0):\n",
        "            print(\"Fail to classify the image. No need to attack.\")\n",
        "            return torch.tensor(x0).cuda()\n",
        "\n",
        "        # 공격을 위한 초기 방향(initial direction) theta 찾기 / g_theta: 거리(짧을수록 좋음)\n",
        "        num_directions = 100\n",
        "        best_theta, g_theta = None, float('inf')\n",
        "        query_count = 0\n",
        "        print(\"Searching for the initial direction on %d random directions: \" % (num_directions))\n",
        "        np.random.seed(args.seed)\n",
        "        timestart = time.time()\n",
        "        for i in range(num_directions):\n",
        "            query_count += 1\n",
        "            theta = np.random.randn(*x0.shape) # 랜덤 theta 샘플링\n",
        "            if model.predict_label(x0 + theta) != y0:\n",
        "                initial_lbd = LA.norm(theta)\n",
        "                theta /= initial_lbd # 단위 방향 벡터\n",
        "                lbd, count = self.fine_grained_binary_search(model, x0, y0, theta, initial_lbd, g_theta)\n",
        "                query_count += count\n",
        "                if lbd < g_theta: # 더 짧은 거리(g_theta)를 찾았다면\n",
        "                    best_theta, g_theta = theta, lbd\n",
        "                    print(\"--------> Found distortion %.4f\" % g_theta)\n",
        "        if g_theta == float('inf'): # 만약 적절한 theta를 찾지 못했다면 500회 더 반복\n",
        "            num_directions = 500\n",
        "            best_theta, g_theta = None, float('inf')\n",
        "            print(\"Searching for the initial direction on %d random directions: \" % (num_directions))\n",
        "            timestart = time.time()\n",
        "            for i in range(num_directions):\n",
        "                query_count += 1\n",
        "                theta = np.random.randn(*x0.shape) # 랜덤 theta 샘플링\n",
        "                if model.predict_label(x0 + theta) != y0:\n",
        "                    initial_lbd = LA.norm(theta)\n",
        "                    theta /= initial_lbd # 단위 방향 벡터\n",
        "                    lbd, count = self.fine_grained_binary_search(model, x0, y0, theta, initial_lbd, g_theta)\n",
        "                    query_count += count\n",
        "                    if lbd < g_theta: # 더 짧은 거리(g_theta)를 찾았다면\n",
        "                        best_theta, g_theta = theta, lbd\n",
        "                        print(\"--------> Found distortion %.4f\" % g_theta)\n",
        "\n",
        "        if g_theta == float('inf'):    \n",
        "            print(\"Couldn't find valid initial, failed\")\n",
        "            return torch.tensor(x0).cuda()\n",
        "        timeend = time.time()\n",
        "        print(\"==========> Found best distortion %.4f in %.4f seconds using %d queries\" % (g_theta, timeend - timestart, query_count))    \n",
        "        self.log[0][0], self.log[0][1] = g_theta, query_count\n",
        "\n",
        "        # 반복적으로 theta를 업데이트하며 공격 수행\n",
        "        timestart = time.time()\n",
        "        g1 = 1.0\n",
        "        theta, g2 = best_theta, g_theta\n",
        "        opt_count = 0\n",
        "        stopping = 0.01\n",
        "        prev_obj = 100000\n",
        "        for i in range(iterations):\n",
        "            # theta의 기울기(gradient) 예측\n",
        "            gradient = np.zeros(theta.shape)\n",
        "            q = 10\n",
        "            min_g1 = float('inf')\n",
        "            for _ in range(q):\n",
        "                u = np.random.randn(*theta.shape)\n",
        "                u /= LA.norm(u)\n",
        "                ttt = theta+beta * u\n",
        "                ttt /= LA.norm(ttt)\n",
        "                g1, count = self.fine_grained_binary_search_local(model, x0, y0, ttt, initial_lbd = g2, tol=beta/500)\n",
        "                opt_count += count\n",
        "                gradient += (g1 - g2) / beta * u # 기울기 구하기\n",
        "                if g1 < min_g1:\n",
        "                    min_g1 = g1\n",
        "                    min_ttt = ttt\n",
        "            gradient = 1.0 / q * gradient\n",
        "\n",
        "            # 쿼리 한도(query limit)를 초과했다면 종료\n",
        "            if opt_count > query_limit:\n",
        "                break\n",
        "\n",
        "            # 공격이 진행되는 상황에서 로깅(logging)\n",
        "            if (i + 1) % 10 == 0:\n",
        "                print(\"Iteration %3d distortion %.4f num_queries %d\" % (i + 1, LA.norm(g2 * theta), opt_count))\n",
        "                prev_obj = g2\n",
        "            self.log[i+1][0], self.log[i+1][1] = g2, opt_count + query_count\n",
        "\n",
        "            if (i + 1) % 50 == 0:\n",
        "                # 화면에 이미지 출력하기\n",
        "                img = torch.tensor(x0 + g2 * theta, dtype=torch.float) # [batch_size, 1, H, W]\n",
        "                img = torch.clamp(img, min=0, max=1).squeeze(0) # [1, H, W]\n",
        "                img = torchvision.transforms.ToPILImage()(img)\n",
        "                plt.imshow(img)\n",
        "                plt.show()\n",
        "\n",
        "            # 예측된 기울기를 이용하여 theta를 업데이트할 것임\n",
        "            min_theta = theta\n",
        "            min_g2 = g2\n",
        "\n",
        "            for _ in range(15): # 예측된 기울기를 이용해 alpha만큼 업데이트했을 때 거리(g_2)가 줄어드는지 확인하여 기록\n",
        "                new_theta = theta - alpha * gradient\n",
        "                new_theta /= LA.norm(new_theta)\n",
        "                new_g2, count = self.fine_grained_binary_search_local(model, x0, y0, new_theta, initial_lbd = min_g2, tol=beta/500)\n",
        "                opt_count += count\n",
        "                alpha = alpha * 2\n",
        "                if new_g2 < min_g2:\n",
        "                    min_theta = new_theta \n",
        "                    min_g2 = new_g2\n",
        "                else:\n",
        "                    break\n",
        "\n",
        "            if min_g2 >= g2: # 필요하다면 alpha를 조금 더 줄여서 거리(g_2)가 줄어드는지 다시 확인하여 기록\n",
        "                for _ in range(15):\n",
        "                    alpha = alpha * 0.25\n",
        "                    new_theta = theta - alpha * gradient\n",
        "                    new_theta /= LA.norm(new_theta)\n",
        "                    new_g2, count = self.fine_grained_binary_search_local(model, x0, y0, new_theta, initial_lbd = min_g2, tol=beta/500)\n",
        "                    opt_count += count\n",
        "                    if new_g2 < g2:\n",
        "                        min_theta = new_theta \n",
        "                        min_g2 = new_g2\n",
        "                        break\n",
        "\n",
        "            # 결과적으로 theta(방향)와 g_theta(거리) 정보를 업데이트\n",
        "            if min_g2 <= min_g1:\n",
        "                theta, g2 = min_theta, min_g2\n",
        "            else:\n",
        "                theta, g2 = min_ttt, min_g1\n",
        "\n",
        "            if g2 < g_theta:\n",
        "                best_theta, g_theta = theta, g2\n",
        "\n",
        "            if alpha < 1e-4:\n",
        "                alpha = 1.0\n",
        "                print(\"Warning: not moving, g2 %lf gtheta %lf\" % (g2, g_theta))\n",
        "                beta = beta * 0.1\n",
        "                if (beta < 1e-8):\n",
        "                   break\n",
        "\n",
        "        target = model.predict_label(x0 + g_theta * best_theta)\n",
        "        timeend = time.time()\n",
        "        print(\"\\nAdversarial Example Found Successfully: distortion %.4f target %d queries %d \\nTime: %.4f seconds\" % (g_theta, target, query_count + opt_count, timeend-timestart))\n",
        "\n",
        "        self.log[i+1:,0] = g_theta\n",
        "        self.log[i+1:,1] = opt_count + query_count\n",
        "        return torch.tensor(x0 + g_theta * best_theta, dtype=torch.float).cuda()\n",
        "\n",
        "    # theta를 업데이트할 때 사용\n",
        "    def fine_grained_binary_search_local(self, model, x0, y0, theta, initial_lbd=1.0, tol=1e-5):\n",
        "        nquery = 0\n",
        "        lbd = initial_lbd\n",
        "\n",
        "        if model.predict_label(x0 + lbd * theta) == y0:\n",
        "            lbd_lo = lbd\n",
        "            lbd_hi = lbd * 1.01\n",
        "            nquery += 1\n",
        "            while model.predict_label(x0 + lbd_hi * theta) == y0:\n",
        "                lbd_hi = lbd_hi * 1.01\n",
        "                nquery += 1\n",
        "                if lbd_hi > 20:\n",
        "                    return float('inf'), nquery\n",
        "        else:\n",
        "            lbd_hi = lbd\n",
        "            lbd_lo = lbd * 0.99\n",
        "            nquery += 1\n",
        "            while model.predict_label(x0 + lbd_lo * theta) != y0 :\n",
        "                lbd_lo = lbd_lo * 0.99\n",
        "                nquery += 1\n",
        "\n",
        "        while (lbd_hi - lbd_lo) > tol:\n",
        "            lbd_mid = (lbd_lo + lbd_hi) / 2.0\n",
        "            nquery += 1\n",
        "            if model.predict_label(x0 + lbd_mid * theta) != y0:\n",
        "                lbd_hi = lbd_mid\n",
        "            else:\n",
        "                lbd_lo = lbd_mid\n",
        "        return lbd_hi, nquery\n",
        "\n",
        "    # 처음에 initial theta를 찾을 때 사용\n",
        "    def fine_grained_binary_search(self, model, x0, y0, theta, initial_lbd, current_best):\n",
        "        nquery = 0\n",
        "        if initial_lbd > current_best: \n",
        "            if model.predict_label(x0 + current_best * theta) == y0:\n",
        "                nquery += 1\n",
        "                return float('inf'), nquery\n",
        "            lbd = current_best\n",
        "        else:\n",
        "            lbd = initial_lbd\n",
        "\n",
        "        lbd_hi = lbd\n",
        "        lbd_lo = 0.0\n",
        "\n",
        "        while (lbd_hi - lbd_lo) > 1e-5:\n",
        "            lbd_mid = (lbd_lo + lbd_hi) / 2.0\n",
        "            nquery += 1\n",
        "            if model.predict_label(x0 + lbd_mid * theta) != y0:\n",
        "                lbd_hi = lbd_mid\n",
        "            else:\n",
        "                lbd_lo = lbd_mid\n",
        "        return lbd_hi, nquery\n",
        "\n",
        "    def __call__(self, input_xi, label_or_target, TARGETED=False, epsilon=None):\n",
        "        if TARGETED:\n",
        "            print(\"Not Implemented.\")\n",
        "        else:\n",
        "            adv = self.attack_untargeted(input_xi, label_or_target)\n",
        "        return adv"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ytpMQL5stsQD"
      },
      "source": [
        "# 단순히 p-norm 거리(distance) 계산\n",
        "def distance(x_adv, x, norm='l2'):\n",
        "    diff = (x_adv - x).view(x.size(0), -1)\n",
        "    if norm == 'l2':\n",
        "        out = torch.sqrt(torch.sum(diff * diff)).item()\n",
        "        return out\n",
        "    elif norm == 'linf':\n",
        "        out = torch.sum(torch.max(torch.abs(diff), 1)[0]).item()\n",
        "        return out"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ksIwqNi9hrEs"
      },
      "source": [
        "from types import SimpleNamespace\n",
        "\n",
        "args = SimpleNamespace()\n",
        "\n",
        "args.attack = 'OPT_attack' # Attack to be used.\n",
        "args.targeted = False # Targeted attack.\n",
        "args.random_start = False # PGD attack with random start.\n",
        "args.epsilon = 1.0\n",
        "args.seed = 123\n",
        "args.norm = 'l2'\n",
        "args.test_batch = 3 # The number of test batch."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Ftqcu-xFmF-i",
        "outputId": "68eb3a69-3337-4a6f-f043-fb6a94596718"
      },
      "source": [
        "amodel = PytorchModel(net, bounds=[0, 1], num_classes=10)\n",
        "attack = OPT_attack(amodel)\n",
        "\n",
        "total_r_count = 0\n",
        "total_clean_count = 0\n",
        "total_distance = 0\n",
        "\n",
        "for i, (xi, yi) in enumerate(test_loader):\n",
        "    print(f\"image batch: {i}\")\n",
        "    if i == args.test_batch:\n",
        "        break\n",
        "\n",
        "    xi, yi = xi.cuda(), yi.cuda()\n",
        "    adv = attack(xi, yi, epsilon=args.epsilon, TARGETED=args.targeted)\n",
        "\n",
        "    # 결과 출력(l_2 distortion이 충분히 감소했을 때 성공)\n",
        "    img = torchvision.transforms.ToPILImage()(torch.clamp(adv, min=0, max=1).squeeze(0))\n",
        "    plt.imshow(img)\n",
        "    plt.show()\n",
        "\n",
        "    if args.targeted == False:\n",
        "        r_count = (torch.max(amodel.predict(adv), 1)[1] == yi).nonzero().shape[0]\n",
        "        clean_count = (torch.max(amodel.predict(xi), 1)[1] == yi).nonzero().shape[0]\n",
        "        total_r_count += r_count\n",
        "        total_clean_count += clean_count\n",
        "        total_distance += distance(adv, xi, norm=args.norm)\n",
        "\n",
        "    if i == 0:\n",
        "        logs = torch.zeros(attack.get_log().size())\n",
        "    logs += attack.get_log()\n",
        "\n",
        "logs /= args.test_batch\n",
        "print(\"saving logs to numpy array\")\n",
        "npy_file = \"log.npy\"\n",
        "np.save(npy_file,logs.numpy())\n",
        "\n",
        "plot_log = np.load(npy_file)\n",
        "plt.plot(plot_log[:,1], plot_log[:,0])\n",
        "plt.ylabel('Distortion')\n",
        "plt.xlabel('Num of queries')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "image batch: 0\n",
            "Searching for the initial direction on 100 random directions: \n",
            "--------> Found distortion 3.7202\n",
            "--------> Found distortion 3.7017\n",
            "--------> Found distortion 3.5716\n",
            "--------> Found distortion 2.9893\n",
            "--------> Found distortion 2.8506\n",
            "--------> Found distortion 2.7356\n",
            "==========> Found best distortion 2.7356 in 3.2823 seconds using 312 queries\n",
            "Iteration  10 distortion 1.8053 num_queries 2103\n",
            "Iteration  20 distortion 1.0979 num_queries 4027\n",
            "Iteration  30 distortion 0.8589 num_queries 5969\n",
            "Iteration  40 distortion 0.7311 num_queries 7772\n",
            "Iteration  50 distortion 0.6423 num_queries 9536\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Iteration  60 distortion 0.5951 num_queries 11179\n",
            "Iteration  70 distortion 0.5617 num_queries 12839\n",
            "Iteration  80 distortion 0.5255 num_queries 14509\n",
            "Iteration  90 distortion 0.4884 num_queries 16200\n",
            "Iteration 100 distortion 0.4657 num_queries 17840\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Iteration 110 distortion 0.4451 num_queries 19486\n",
            "\n",
            "Adversarial Example Found Successfully: distortion 0.4412 target 6 queries 20440 \n",
            "Time: 212.0795 seconds\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "image batch: 1\n",
            "Searching for the initial direction on 100 random directions: \n",
            "--------> Found distortion 4.5299\n",
            "--------> Found distortion 4.0619\n",
            "--------> Found distortion 3.9259\n",
            "--------> Found distortion 3.8379\n",
            "--------> Found distortion 3.4344\n",
            "--------> Found distortion 3.3284\n",
            "==========> Found best distortion 3.3284 in 3.1464 seconds using 312 queries\n",
            "Iteration  10 distortion 2.0991 num_queries 2245\n",
            "Iteration  20 distortion 1.7654 num_queries 4296\n",
            "Iteration  30 distortion 1.4610 num_queries 6263\n",
            "Iteration  40 distortion 1.0342 num_queries 8254\n",
            "Iteration  50 distortion 0.7985 num_queries 10143\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Iteration  60 distortion 0.6608 num_queries 11884\n",
            "Iteration  70 distortion 0.5963 num_queries 13592\n",
            "Iteration  80 distortion 0.5478 num_queries 15331\n",
            "Iteration  90 distortion 0.5143 num_queries 17071\n",
            "Iteration 100 distortion 0.4827 num_queries 18770\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Adversarial Example Found Successfully: distortion 0.4606 target 1 queries 20410 \n",
            "Time: 212.6560 seconds\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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uOl+PGyN15qyItDyqh69ZFrnzSQctAMDwKJfexiL3cMV5hubCSlg7PHue74svt8ISZjtyUdYMdnf/cGD4S2vNE0JcW+gbdEIkgoJdiERQsAuRCAp2IRJBwS5EIgy04GRZllhaDksGv/Jrh+i83/hn/zQ4PrGDZ3K98/C7qK36T95Nbc1IscGxRlhOqg7x7LVqnduKkmevdTMu5y1Eii92snDhzizS/ulth/jaX7//bdQ2e4nLckPj4YKIufPCot6JZGxF2kbF1nGlHc4Oay3x7LuixY+31OKZea/85KfUtrrKU/NWl8LHLAruxwgp9ll0VHBSiORRsAuRCAp2IRJBwS5EIijYhUgEBbsQiTBQ6a3eGML0wduCtvvu+5d03mIRlmueO/EKnWPOJa8y0mOt2+ES1Qzp15XPclmlcC7xVLmahLZxCWVhIVzMEQDyM+HssLMzXK5bXImkr3V4wZGxSEbf/POvBsdfevEEP1eVy55TkZ5z7RbvfTczE84cm53jfeU8Il/Vqzz7znN+7wxX+Pvq5FBYpqxF0u9W5sP9D8uyS+fonV2IRFCwC5EICnYhEkHBLkQiKNiFSISB7sZPTk3hw7/3e0Hb2A376Lxjx8K77i2S5AAAnUjNr04esbX5jmqtEl4uiyRwoMt3ios8Ujut5DZzniDRsbBtfoYrF/NdvuNe6/Kd6Z1N3tqqRXbIL1wM7yIDQB7ZzZ6d4de61eU75Msk4cUjc7IaX/tKl++QD9X5fZUZn7e8FF7jEvy+auwIHy+SL6R3diFSQcEuRCIo2IVIBAW7EImgYBciERTsQiTCeto/TQP4MwB70Gv3dMTdP29mkwC+BuAAei2gPuTul2LHWlpewuM//EHQ9sQzT3Iny3AiTGVomM7JnCdVWI3bKjk/JqNW5cs4PBR5Pa3yc9VHeSKPRWScqoWf22iFy2SNGpehOjlPrJgruA0Wlo2GhnnyTHeey2tL8zxxZaXk0mGVSIcFuMzXjUibyzm3zc4uUNtYk99zuyfD1yaPJM/UG+HjbVR6KwD8sbsfAvBOAH9kZocAPAjgMXe/GcBj/Z+FENcoawa7u59x9yf7jxcAHAewD8C9AB7u/9rDAD6wVU4KITbOG/qb3cwOALgDwOMA9rj7mb7pVfQ+5gshrlHWHexmNgrgGwA+7u6v+c6juzt6f8+H5j1gZkfN7Gi7tbIhZ4UQV8+6gt3MqugF+pfd/Zv94bNmtrdv3wvgXGiuux9x98PufrhW5z3HhRBby5rBbmaGXj/24+7+2StMjwC4v//4fgDf3nz3hBCbxXqy3t4F4CMAnjGzp/pjnwDwaQBfN7OPAjgB4ENrHWhxcR7/9/98N2hbvchVu1oelmuGJsfonLLFs7Vq4JJXO/LyZ42wsRbJ1mpW+aeZ6ghvhVRvcFs+wp93NZ8Ijjdy7odVeXZVNeMyn4Fn9LVJVlkxx2W+Vs4ltJJk8wGAF/yY7NrE2msh52s/NcUl0WaN614jXHlDBeF7tWr8mnVXw8/ZI7LhmsHu7n8HgD2L31xrvhDi2kDfoBMiERTsQiSCgl2IRFCwC5EICnYhEmGgBScrlRy7doZlozMrZ+m87mpYlhvbwSWowpvU1prnMt/SDLc5kY3apC0UAJxpcVnIcq7HeMblvEZ9L7V1iUwZy/JCpABnc4TLlKNN3kartRCW0SrG31+8wf0oI9JVI9I2angoLJXtafLsu93jXG7cd91OamsO8+e2ujJDbYZwUcxaRB6cmLguOF6JFTGlFiHEmwoFuxCJoGAXIhEU7EIkgoJdiERQsAuRCAOV3sy7yIqwzDA2zDONFivhohcLrfCxAOCOX72R+zHLJbuz57nt1bNh+WR+OSLltbn05l1eYBFdnr00OsR9vPm2Q8HxU8s8Q23m4kVqW1zlRRQX5/kxqxYuRlnLI8U5jct8u4d5BthURIKdmr4+OP7L01xCu77Gfbw4x3vVLbzK1xGRwqPDY+Hn3djB5cHJ8XB2Y4X0IwT0zi5EMijYhUgEBbsQiaBgFyIRFOxCJMJAd+OLdgcXTp4M2ryM1DPLwgkSK8un6ZzTxyJ1xOrcVuvwHfLRWniHub3Ed85Xc54kk5W8fVIXPAlifiVYyBcA8O677gyOHzp0K51z5uQpajt3kZ+rs7JKbd4NJ95klUidtpw/5511vjM9FklqaeVhNeT82Z/TOT8+z5NWIuULMbaL7/APj3Ifh5o7guO7d03SOaM7wi2jskgCld7ZhUgEBbsQiaBgFyIRFOxCJIKCXYhEULALkQhrSm9mNg3gz9BryewAjrj7583sUwB+H8D5/q9+wt2/Ez1ZLcfu6bCccPLFM8FxAGgXROLpcMnr5eM/pra5SIPJzLg0tEiSO+ZLnpCDSDse3mgHKCN+tDu8TdIP//7R4Ph7m/xS3wa+HosTPMnERnidv7IbllLbBZdY5zs8aejMRS6Vnfjpq9R2cTl87yxHJNGRPVxC27mXdybPRnntujxyzw2Ph6W32iiv8VchdQMjt826dPYCwB+7+5Nm1gTwhJldvqM+5+7/cR3HEEJsM+vp9XYGwJn+4wUzOw5g31Y7JoTYXN7Q3+xmdgDAHQAe7w99zMyeNrOHzCycYCuEuCZYd7Cb2SiAbwD4uLvPA/gCgIMAbkfvnf8zZN4DZnbUzI52u/zroUKIrWVdwW5mVfQC/cvu/k0AcPez7t519xLAFwHcFZrr7kfc/bC7H84jfcyFEFvLmsFuZgbgSwCOu/tnrxi/si3JBwEc23z3hBCbxXp2498F4CMAnjGzp/pjnwDwYTO7HT057mUAf7DWgWr1Gm48GK4NtzjPa521ToflmtK5ztAyLk/NrHJbw8PtggCgRdrx8FwtACW3dkg2HwBUIjXoSnDZ6IUnHw+On1zg8uDOjD/nElxO8oz7uFCGba9k4XqCAPDSIs84PJ3xDLuVOvexuS/cJmnPLTfQOY0dPNusjLTsqkTWsT7OQ21kMpwRl3V5il27El7fSEisazf+7xAWhKOauhDi2kLfoBMiERTsQiSCgl2IRFCwC5EICnYhEmGgBScrlQompsKF8vZeN0XnvXLmQnA8j3whr4gmm/HXuAI886pD5CQHP1n01ZQcby3KiNhXrC4GxxcjrYm8wTPb8nZ47QHgVIVLgE8V4XV8sc4v2sIol65Gr+M+TuzdT227doez1GrDvABk7B4onF/RSuRiN3IuDxpZxqzJ749aFg5di2RS6p1diERQsAuRCAp2IRJBwS5EIijYhUgEBbsQiTBQ6c0sR14JF9erDPHienkt/JrUbXMJKtJSDEWsCGSkYp+V5LUx0rOtBM/hN/B5RSz1v8sv2wKR5Y47zyocMy55/aQ4T23PzvNMugvj4es5tZdnm+27KZwRCQBj+3lxy8YIzw6rdMPXrHQur2XVSPZajfcJHG1wP8qIZOekx51F3ovNmJQn6U2I5FGwC5EICnYhEkHBLkQiKNiFSAQFuxCJMFDpzVGi8HCxx4WlOTpvcjIshcxd4oUjnSdkoRLR5YqIjMaSoTyPvGbGUvM6kYKTkUy6osbnLZCKg3+/Mk/nvEx62AHAwhAvsGg38Gyz6/buCo4fiGQ37mryQo/W4NLsSqTi5wJ5brWMa5sjQ1x6G6pxCbA6zO+DqDw4HLZVKjxTLuuSXm90ht7ZhUgGBbsQiaBgFyIRFOxCJIKCXYhEWHM33swaAL4PoN7//T9390+a2U0AvgpgCsATAD7iTrba+5TuaJXhNj6VBt9SHd8R3okdHh2ic7rLfF+yE3FzNbKLz+p7mXPfax2+q96NFC0rIzv8ZeSy1UjzzNURvrO7PM53wQ9M7KS2iXFexy3fEd7RHhvhz6va4LvgnQ6/Zq3IW5ZXw2tVrXGVISf13QCgiETMcMbvx6zG13+EKCjVGj9ZSe65WFXD9byztwC8191vQ6898z1m9k4AfwLgc+7+VgCXAHx0HccSQmwTawa797hcsrTa/+cA3gvgz/vjDwP4wJZ4KITYFNbbnz3vd3A9B+BRAC8AmHX//19dOQVg39a4KITYDNYV7O7edffbAewHcBeAX1rvCczsATM7amZH26vh1stCiK3nDe3Gu/ssgL8F8I8BjJvZ5R2E/QBOkzlH3P2wux+uRTZghBBby5rBbma7zGy8/3gIwG8BOI5e0P9u/9fuB/DtrXJSCLFx1pMIsxfAw2aWo/fi8HV3/0szew7AV83s3wP4IYAvrXWgzAz1aljyaE5M0HkjRLboRtonFW1uc0QUwkh9ty7LnSh4XbJKpEWSG0/GqBLJqGejJuyohZ93fZi3T9rT5LZmnctJIxXuSJUkd7QrPOlmhatTWCq4lLqa8Ws9QtouZRFps5pFHInVKIwc09sr1NYqyRov8XunPsL84GuxZrC7+9MA7giMv4je3+9CiF8A9A06IRJBwS5EIijYhUgEBbsQiaBgFyIRzGOtkDb7ZGbnAZzo/7gTwIWBnZwjP16L/Hgtv2h+3OjuwQKAAw3215zY7Ki7H96Wk8sP+ZGgH/oYL0QiKNiFSITtDPYj23juK5Efr0V+vJY3jR/b9je7EGKw6GO8EImwLcFuZveY2U/M7Hkze3A7fOj78bKZPWNmT5nZ0QGe9yEzO2dmx64YmzSzR83sZ/3/eRrg1vrxKTM73V+Tp8zsfQPwY9rM/tbMnjOzZ83sX/XHB7omET8GuiZm1jCzfzCzH/X9+Hf98ZvM7PF+3HzNzCLpeQHcfaD/AOTolbV6C4AagB8BODRoP/q+vAxg5zac99cB3Ang2BVj/wHAg/3HDwL4k23y41MA/vWA12MvgDv7j5sAfgrg0KDXJOLHQNcEvZZto/3HVQCPA3gngK8DuK8//p8B/OEbOe52vLPfBeB5d3/Re6Wnvwrg3m3wY9tw9+8DuPi64XvRK9wJDKiAJ/Fj4Lj7GXd/sv94Ab3iKPsw4DWJ+DFQvMemF3ndjmDfB+DkFT9vZ7FKB/BdM3vCzB7YJh8us8fdz/Qfvwpgzzb68jEze7r/MX/L/5y4EjM7gF79hMexjWvyOj+AAa/JVhR5TX2D7t3ufieA3wbwR2b269vtENB7ZUe83v9W8gUAB9HrEXAGwGcGdWIzGwXwDQAfd/fX9Jge5JoE/Bj4mvgGirwytiPYTwOYvuJnWqxyq3H30/3/zwH4Fra38s5ZM9sLAP3/z22HE+5+tn+jlQC+iAGtiZlV0QuwL7v7N/vDA1+TkB/btSb9c7/hIq+M7Qj2HwC4ub+zWANwH4BHBu2EmY2YWfPyYwB3AzgWn7WlPIJe4U5gGwt4Xg6uPh/EANbEzAy9GobH3f2zV5gGuibMj0GvyZYVeR3UDuPrdhvfh95O5wsA/s02+fAW9JSAHwF4dpB+APgKeh8HO+j97fVR9HrmPQbgZwC+B2Bym/z4bwCeAfA0esG2dwB+vBu9j+hPA3iq/+99g16TiB8DXRMAv4JeEden0Xth+bdX3LP/AOB5AP8TQP2NHFffoBMiEVLfoBMiGRTsQiSCgl2IRFCwC5EICnYhEkHBLkQiKNiFSAQFuxCJ8P8AmlyzVzJsDVwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "image batch: 2\n",
            "Searching for the initial direction on 100 random directions: \n",
            "--------> Found distortion 2.2699\n",
            "--------> Found distortion 1.8510\n",
            "--------> Found distortion 1.6913\n",
            "--------> Found distortion 1.5024\n",
            "==========> Found best distortion 1.5024 in 2.7768 seconds using 273 queries\n",
            "Iteration  10 distortion 0.8068 num_queries 1969\n",
            "Iteration  20 distortion 0.6085 num_queries 3722\n",
            "Iteration  30 distortion 0.5042 num_queries 5532\n",
            "Iteration  40 distortion 0.4492 num_queries 7233\n",
            "Iteration  50 distortion 0.4066 num_queries 8893\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Iteration  60 distortion 0.3604 num_queries 10573\n",
            "Iteration  70 distortion 0.3374 num_queries 12166\n",
            "Iteration  80 distortion 0.3159 num_queries 13751\n",
            "Iteration  90 distortion 0.2970 num_queries 15362\n",
            "Iteration 100 distortion 0.2859 num_queries 16886\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Iteration 110 distortion 0.2694 num_queries 18447\n",
            "Iteration 120 distortion 0.2587 num_queries 19946\n",
            "\n",
            "Adversarial Example Found Successfully: distortion 0.2581 target 9 queries 20365 \n",
            "Time: 214.3301 seconds\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "image batch: 3\n",
            "saving logs to numpy array\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}