security_3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyOvjw+epyC4GISHj3Q8VmjM",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ssvakil/Machine-Learning-in-Python-Workshop/blob/master/security_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from keras.datasets import mnist\n",
        "!pip install matplotlib\n",
        "from matplotlib import pyplot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J5gC-jR4txGd",
        "outputId": "808466bd-c758-49dc-bd2e-7f7ec81fc350"
      },
      "execution_count": 12,
      "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: matplotlib in /usr/local/lib/python3.9/dist-packages (3.7.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (0.11.0)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (1.0.7)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: importlib-resources>=3.2.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (5.12.0)\n",
            "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (1.22.4)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (8.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (3.0.9)\n",
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            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (4.39.3)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib) (23.0)\n",
            "Requirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.9/dist-packages (from importlib-resources>=3.2.0->matplotlib) (3.15.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-D_YJeKPl9mJ",
        "outputId": "6dc92f26-eac2-4c88-845e-738a9cc89201"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Meta-Nerual-Trojan-Detection'...\n",
            "remote: Enumerating objects: 32, done.\u001b[K\n",
            "remote: Counting objects: 100% (32/32), done.\u001b[K\n",
            "remote: Compressing objects: 100% (23/23), done.\u001b[K\n",
            "remote: Total 32 (delta 6), reused 32 (delta 6), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (32/32), 493.46 KiB | 3.27 MiB/s, done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/AI-secure/Meta-Nerual-Trojan-Detection.git"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cd Meta-Nerual-Trojan-Detection"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aI0TliaxmMtt",
        "outputId": "5af9d8d0-f1d6-46ae-c1f9-d4361af6d59f"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/Meta-Nerual-Trojan-Detection\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -r /content/Meta-Nerual-Trojan-Detection/requirements.txt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RY0a2v5Xmq4j",
        "outputId": "e5122e1f-9ad3-4e8d-b6ee-2f0012424052"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  Downloading llvmlite-0.31.0.tar.gz (110 kB)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting numba==0.48.0\n",
            "  Downloading numba-0.48.0.tar.gz (2.0 MB)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting numpy==1.19.2\n",
            "  Downloading numpy-1.19.2.zip (7.3 MB)\n",
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            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting Pillow==7.2.0\n",
            "  Downloading Pillow-7.2.0.tar.gz (39.1 MB)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: Ignored the following versions that require a different python version: 0.52.0 Requires-Python >=3.6,<3.9; 0.52.0rc3 Requires-Python >=3.6,<3.9\u001b[0m\u001b[31m\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install kaggle"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wPQfglz8nM3d",
        "outputId": "3a67b3cb-5261-48d9-9e6e-53090b2724ea"
      },
      "execution_count": 4,
      "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: kaggle in /usr/local/lib/python3.9/dist-packages (1.5.13)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.9/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from kaggle) (2.27.1)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.9/dist-packages (from kaggle) (8.0.1)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.9/dist-packages (from kaggle) (1.16.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.9/dist-packages (from kaggle) (2022.12.7)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.9/dist-packages (from kaggle) (1.26.15)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.9/dist-packages (from kaggle) (4.65.0)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.9/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->kaggle) (3.4)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests->kaggle) (2.0.12)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "id": "1MZ0Xg2BpYcf",
        "outputId": "220b1fd4-1896-43e7-a9e5-4e521566c619"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-53ab928d-316a-4b5a-9213-89c691ddcb0d\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-53ab928d-316a-4b5a-9213-89c691ddcb0d\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"ssvakil\",\"key\":\"a1406fa3a1a6bf44ef5a1a57613c53b6\"}'}"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/kaggle.json\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "id": "R_NKTDCwpsuz"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d pankrzysiu/cifar10-python"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z77faORqqudk",
        "outputId": "da700738-2cc9-4205-90d2-37c898169c00"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading cifar10-python.zip to /content/Meta-Nerual-Trojan-Detection\n",
            " 94% 306M/325M [00:02<00:00, 168MB/s]\n",
            "100% 325M/325M [00:02<00:00, 122MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d lijiyu/imagenet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2NFOVbwlsfS_",
        "outputId": "133f637a-0e90-4655-bfdb-120c17140a67"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading imagenet.zip to /content/Meta-Nerual-Trojan-Detection\n",
            "100% 6.20G/6.21G [00:47<00:00, 130MB/s]\n",
            "100% 6.21G/6.21G [00:47<00:00, 139MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d bistaumanga/usps-dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "roo1aXE0s0qo",
        "outputId": "7593048b-4eb4-4748-b0cb-e5d4a04be7b1"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading usps-dataset.zip to /content/Meta-Nerual-Trojan-Detection\n",
            "\r  0% 0.00/2.75M [00:00<?, ?B/s]\n",
            "\r100% 2.75M/2.75M [00:00<00:00, 216MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "keras.models.load_model(\"354rdd.h5\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lfTata57s3pB",
        "outputId": "32ebfbf8-dbdc-4eb0-a08b-de921230b352"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.engine.sequential.Sequential at 0x7ff3f090cfa0>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "keras.models.load_model(\"7gdf823.h5\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AWjciHvot-nA",
        "outputId": "06a64e73-9c12-4b82-e711-df924f105006"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.engine.sequential.Sequential at 0x7ff3f091ed00>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from zipfile import ZipFile\n",
        "file_name=\"cifar10-python.zip\"\n",
        "with ZipFile(file_name,'r')as zip:\n",
        "  zip.extractall()\n",
        "print('done')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0fRQXVAhuchc",
        "outputId": "8c962917-52cb-44be-c706-c0f0fc058f43"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "done\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from zipfile import ZipFile\n",
        "file_name=\"imagenet.zip\"\n",
        "with ZipFile(file_name,'r')as zip:\n",
        "  zip.extractall()\n",
        "print('done')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EWtXwKnluclb",
        "outputId": "92cc38c7-addc-4717-969d-54ae0f08b9a7"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "done\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from zipfile import ZipFile\n",
        "file_name=\"usps-dataset.zip\"\n",
        "with ZipFile(file_name,'r')as zip:\n",
        "  zip.extractall()\n",
        "print('done')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8JveHoDvucpn",
        "outputId": "cf4124a3-3472-4a13-c8cd-3239e187339e"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "done\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python3 /content/Meta-Nerual-Trojan-Detection/meta_classifier.py /content/Meta-Nerual-Trojan-Detection/354rdd.h5"
      ],
      "metadata": {
        "id": "1Qyi2J7EvPql"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!python3 /content/Meta-Nerual-Trojan-Detection/meta_classifier.py /content/Meta-Nerual-Trojan-Detection/7gdf823.h5"
      ],
      "metadata": {
        "id": "ocZXvkSKWKt9"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cd raw_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YIvIvusowOHJ",
        "outputId": "0767f8ae-cd22-4ec8-e72f-5646f1f08b0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/Meta-Nerual-Trojan-Detection/raw_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pwd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "ntEgD4GYwgk1",
        "outputId": "04e8aa25-0572-468f-d7ff-fb1b4045b0d2"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/Meta-Nerual-Trojan-Detection'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python3 /content/Meta-Nerual-Trojan-Detection/run_meta_oneclass.py /content/Meta-Nerual-Trojan-Detection/354rdd.h5"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mznBQCHBWab5",
        "outputId": "01c7b82d-a63b-4bca-e680-a82c41a22cc4"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "usage: run_meta_oneclass.py\n",
            "       [-h]\n",
            "       --task\n",
            "       TASK\n",
            "       --troj_type\n",
            "       TROJ_TYPE\n",
            "       [--load_exist]\n",
            "run_meta_oneclass.py: error: the following arguments are required: --task, --troj_type\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python /content/Meta-Nerual-Trojan-Detection/train_basic_benign.py /content/Meta-Nerual-Trojan-Detection/raw_data/rt_polarity/rt-polarity.neg"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tv1wNZ9IXHLZ",
        "outputId": "bf7c0f17-f7cf-4599-8ef8-78c763c1b03f"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "usage: train_basic_benign.py\n",
            "       [-h]\n",
            "       --task\n",
            "       TASK\n",
            "train_basic_benign.py: error: the following arguments are required: --task\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip3 install torch==1.2.0+cu92 torchvision==0.4.0+cu92 -f https://download.pytorch.org/whl/torch_stable.html"
      ],
      "metadata": {
        "id": "410Kjy72aevZ",
        "outputId": "09223bef-5c89-4523-b30f-75719765921d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Looking in links: https://download.pytorch.org/whl/torch_stable.html\n",
            "\u001b[31mERROR: Could not find a version that satisfies the requirement torch==1.2.0+cu92 (from versions: 1.7.1, 1.7.1+cpu, 1.7.1+cu101, 1.7.1+cu110, 1.7.1+cu92, 1.7.1+rocm3.7, 1.7.1+rocm3.8, 1.8.0, 1.8.0+cpu, 1.8.0+cu101, 1.8.0+cu111, 1.8.0+rocm3.10, 1.8.0+rocm4.0.1, 1.8.1, 1.8.1+cpu, 1.8.1+cu101, 1.8.1+cu102, 1.8.1+cu111, 1.8.1+rocm3.10, 1.8.1+rocm4.0.1, 1.9.0, 1.9.0+cpu, 1.9.0+cu102, 1.9.0+cu111, 1.9.0+rocm4.0.1, 1.9.0+rocm4.1, 1.9.0+rocm4.2, 1.9.1, 1.9.1+cpu, 1.9.1+cu102, 1.9.1+cu111, 1.9.1+rocm4.0.1, 1.9.1+rocm4.1, 1.9.1+rocm4.2, 1.10.0, 1.10.0+cpu, 1.10.0+cu102, 1.10.0+cu111, 1.10.0+cu113, 1.10.0+rocm4.0.1, 1.10.0+rocm4.1, 1.10.0+rocm4.2, 1.10.1, 1.10.1+cpu, 1.10.1+cu102, 1.10.1+cu111, 1.10.1+cu113, 1.10.1+rocm4.0.1, 1.10.1+rocm4.1, 1.10.1+rocm4.2, 1.10.2, 1.10.2+cpu, 1.10.2+cu102, 1.10.2+cu111, 1.10.2+cu113, 1.10.2+rocm4.0.1, 1.10.2+rocm4.1, 1.10.2+rocm4.2, 1.11.0, 1.11.0+cpu, 1.11.0+cu102, 1.11.0+cu113, 1.11.0+cu115, 1.11.0+rocm4.3.1, 1.11.0+rocm4.5.2, 1.12.0, 1.12.0+cpu, 1.12.0+cu102, 1.12.0+cu113, 1.12.0+cu116, 1.12.0+rocm5.0, 1.12.0+rocm5.1.1, 1.12.1, 1.12.1+cpu, 1.12.1+cu102, 1.12.1+cu113, 1.12.1+cu116, 1.12.1+rocm5.0, 1.12.1+rocm5.1.1, 1.13.0, 1.13.0+cpu, 1.13.0+cu116, 1.13.0+cu117, 1.13.0+cu117.with.pypi.cudnn, 1.13.0+rocm5.1.1, 1.13.0+rocm5.2, 1.13.1, 1.13.1+cpu, 1.13.1+cu116, 1.13.1+cu117, 1.13.1+cu117.with.pypi.cudnn, 1.13.1+rocm5.1.1, 1.13.1+rocm5.2, 2.0.0, 2.0.0+cpu, 2.0.0+cpu.cxx11.abi, 2.0.0+cu117, 2.0.0+cu117.with.pypi.cudnn, 2.0.0+cu118, 2.0.0+rocm5.3, 2.0.0+rocm5.4.2)\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[31mERROR: No matching distribution found for torch==1.2.0+cu92\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms"
      ],
      "metadata": {
        "id": "H5390vB3bGSp"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(torch.__version__)"
      ],
      "metadata": {
        "id": "kZX5BJT1bLeE",
        "outputId": "9212ce1f-6d96-4728-e6e9-20e6b8b21fcf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.0.0+cu118\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip uninstall -y mxnet && pip install mxnet-cu90==1.1.0"
      ],
      "metadata": {
        "id": "w9LsCvFUdbU2",
        "outputId": "a85a2bb6-41a4-4a94-9256-c89162b30cc8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[33mWARNING: Skipping mxnet as it is not installed.\u001b[0m\u001b[33m\n",
            "\u001b[0mLooking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting mxnet-cu90==1.1.0\n",
            "  Downloading mxnet_cu90-1.1.0-py2.py3-none-manylinux1_x86_64.whl (307.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m307.5/307.5 MB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting graphviz==0.8.1\n",
            "  Downloading graphviz-0.8.1-py2.py3-none-any.whl (17 kB)\n",
            "Collecting requests==2.18.4\n",
            "  Downloading requests-2.18.4-py2.py3-none-any.whl (88 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m88.7/88.7 kB\u001b[0m \u001b[31m14.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting numpy<=1.13.3\n",
            "  Downloading numpy-1.13.3.zip (5.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.0/5.0 MB\u001b[0m \u001b[31m93.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting chardet<3.1.0,>=3.0.2\n",
            "  Using cached chardet-3.0.4-py2.py3-none-any.whl (133 kB)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests==2.18.4->mxnet-cu90==1.1.0) (2022.12.7)\n",
            "Collecting idna<2.7,>=2.5\n",
            "  Downloading idna-2.6-py2.py3-none-any.whl (56 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.5/56.5 kB\u001b[0m \u001b[31m8.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting urllib3<1.23,>=1.21.1\n",
            "  Downloading urllib3-1.22-py2.py3-none-any.whl (132 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.3/132.3 kB\u001b[0m \u001b[31m20.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hBuilding wheels for collected packages: numpy\n",
            "  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
            "  \n",
            "  \u001b[31m×\u001b[0m \u001b[32mpython setup.py bdist_wheel\u001b[0m did not run successfully.\n",
            "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
            "  \u001b[31m╰─>\u001b[0m See above for output.\n",
            "  \n",
            "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
            "  Building wheel for numpy (setup.py) ... \u001b[?25lerror\n",
            "\u001b[31m  ERROR: Failed building wheel for numpy\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[?25h  Running setup.py clean for numpy\n",
            "  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
            "  \n",
            "  \u001b[31m×\u001b[0m \u001b[32mpython setup.py clean\u001b[0m did not run successfully.\n",
            "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
            "  \u001b[31m╰─>\u001b[0m See above for output.\n",
            "  \n",
            "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
            "\u001b[31m  ERROR: Failed cleaning build dir for numpy\u001b[0m\u001b[31m\n",
            "\u001b[0mFailed to build numpy\n",
            "Installing collected packages: urllib3, idna, graphviz, chardet, requests, numpy, mxnet-cu90\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.26.15\n",
            "    Uninstalling urllib3-1.26.15:\n",
            "      Successfully uninstalled urllib3-1.26.15\n",
            "  Attempting uninstall: idna\n",
            "    Found existing installation: idna 3.4\n",
            "    Uninstalling idna-3.4:\n",
            "      Successfully uninstalled idna-3.4\n",
            "  Attempting uninstall: graphviz\n",
            "    Found existing installation: graphviz 0.20.1\n",
            "    Uninstalling graphviz-0.20.1:\n",
            "      Successfully uninstalled graphviz-0.20.1\n",
            "  Attempting uninstall: chardet\n",
            "    Found existing installation: chardet 4.0.0\n",
            "    Uninstalling chardet-4.0.0:\n",
            "      Successfully uninstalled chardet-4.0.0\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.27.1\n",
            "    Uninstalling requests-2.27.1:\n",
            "      Successfully uninstalled requests-2.27.1\n",
            "  Attempting uninstall: numpy\n",
            "    Found existing installation: numpy 1.22.4\n",
            "    Uninstalling numpy-1.22.4:\n",
            "      Successfully uninstalled numpy-1.22.4\n",
            "  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
            "  \n",
            "  \u001b[31m×\u001b[0m \u001b[32mRunning setup.py install for numpy\u001b[0m did not run successfully.\n",
            "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
            "  \u001b[31m╰─>\u001b[0m See above for output.\n",
            "  \n",
            "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
            "  Running setup.py install for numpy ... \u001b[?25l\u001b[?25herror\n",
            "  Rolling back uninstall of numpy\n",
            "  Moving to /usr/local/bin/f2py\n",
            "   from /tmp/pip-uninstall-e5voa9hk/f2py\n",
            "  Moving to /usr/local/bin/f2py3\n",
            "   from /tmp/pip-uninstall-e5voa9hk/f2py3\n",
            "  Moving to /usr/local/bin/f2py3.9\n",
            "   from /tmp/pip-uninstall-e5voa9hk/f2py3.9\n",
            "  Moving to /usr/local/lib/python3.9/dist-packages/numpy-1.22.4.dist-info/\n",
            "   from /usr/local/lib/python3.9/dist-packages/~umpy-1.22.4.dist-info\n",
            "  Moving to /usr/local/lib/python3.9/dist-packages/numpy.libs/\n",
            "   from /usr/local/lib/python3.9/dist-packages/~umpy.libs\n",
            "  Moving to /usr/local/lib/python3.9/dist-packages/numpy/\n",
            "   from /usr/local/lib/python3.9/dist-packages/~umpy\n",
            "\u001b[1;31merror\u001b[0m: \u001b[1mlegacy-install-failure\u001b[0m\n",
            "\n",
            "\u001b[31m×\u001b[0m Encountered error while trying to install package.\n",
            "\u001b[31m╰─>\u001b[0m numpy\n",
            "\n",
            "\u001b[1;35mnote\u001b[0m: This is an issue with the package mentioned above, not pip.\n",
            "\u001b[1;36mhint\u001b[0m: See above for output from the failure.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python3 /content/Meta-Nerual-Trojan-Detection/train_basic_benign.py --task mnist\n",
        "!python /content/Meta-Nerual-Trojan-Detection/train_basic_jumbo.py --task mnist\n",
        "!python /content/Meta-Nerual-Trojan-Detection/train_basic_trojaned.py --task mnist --troj_type M\n",
        "!python /content/Meta-Nerual-Trojan-Detection/train_basic_trojaned.py --task mnist --troj_type B"
      ],
      "metadata": {
        "id": "U3aBRorYZ8-a",
        "outputId": "81b68eb6-d7c0-42b9-cfef-56a25fa7fb8f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data indices owned by the defender: [ 2732 43567 42613 ... 36134  7078  8804]\n",
            "Data indices owned by the attacker: [20929 58997 33339 ... 30107 45814 21000]\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/Meta-Nerual-Trojan-Detection/train_basic_benign.py\", line 50, in <module>\n",
            "    model = Model(gpu=GPU)\n",
            "  File \"/content/Meta-Nerual-Trojan-Detection/model_lib/mnist_cnn_model.py\", line 18, in __init__\n",
            "    self.cuda()\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py\", line 905, in cuda\n",
            "    return self._apply(lambda t: t.cuda(device))\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py\", line 797, in _apply\n",
            "    module._apply(fn)\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py\", line 820, in _apply\n",
            "    param_applied = fn(param)\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py\", line 905, in <lambda>\n",
            "    return self._apply(lambda t: t.cuda(device))\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/torch/cuda/__init__.py\", line 247, in _lazy_init\n",
            "    torch._C._cuda_init()\n",
            "RuntimeError: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx\n",
            "Data indices owned by the defender: [ 2732 43567 42613 ... 36134  7078  8804]\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/Meta-Nerual-Trojan-Detection/train_basic_jumbo.py\", line 43, in <module>\n",
            "    model = Model(gpu=GPU)\n",
            "  File \"/content/Meta-Nerual-Trojan-Detection/model_lib/mnist_cnn_model.py\", line 18, in __init__\n",
            "    self.cuda()\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py\", line 905, in cuda\n",
            "    return self._apply(lambda t: t.cuda(device))\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py\", line 797, in _apply\n",
            "    module._apply(fn)\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py\", line 820, in _apply\n",
            "    param_applied = fn(param)\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py\", line 905, in <lambda>\n",
            "    return self._apply(lambda t: t.cuda(device))\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/torch/cuda/__init__.py\", line 247, in _lazy_init\n",
            "    torch._C._cuda_init()\n",
            "RuntimeError: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx\n",
            "Data indices owned by the attacker: [20929 58997 33339 ... 30107 45814 21000]\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/Meta-Nerual-Trojan-Detection/train_basic_trojaned.py\", line 45, in <module>\n",
            "    model = Model(gpu=GPU)\n",
            "  File \"/content/Meta-Nerual-Trojan-Detection/model_lib/mnist_cnn_model.py\", line 18, in __init__\n",
            "    self.cuda()\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py\", line 905, in cuda\n",
            "    return self._apply(lambda t: t.cuda(device))\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py\", line 797, in _apply\n",
            "    module._apply(fn)\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py\", line 820, in _apply\n",
            "    param_applied = fn(param)\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py\", line 905, in <lambda>\n",
            "    return self._apply(lambda t: t.cuda(device))\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/torch/cuda/__init__.py\", line 247, in _lazy_init\n",
            "    torch._C._cuda_init()\n",
            "RuntimeError: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx\n",
            "Data indices owned by the attacker: [20929 58997 33339 ... 30107 45814 21000]\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/Meta-Nerual-Trojan-Detection/train_basic_trojaned.py\", line 45, in <module>\n",
            "    model = Model(gpu=GPU)\n",
            "  File \"/content/Meta-Nerual-Trojan-Detection/model_lib/mnist_cnn_model.py\", line 18, in __init__\n",
            "    self.cuda()\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py\", line 905, in cuda\n",
            "    return self._apply(lambda t: t.cuda(device))\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py\", line 797, in _apply\n",
            "    module._apply(fn)\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py\", line 820, in _apply\n",
            "    param_applied = fn(param)\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py\", line 905, in <lambda>\n",
            "    return self._apply(lambda t: t.cuda(device))\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/torch/cuda/__init__.py\", line 247, in _lazy_init\n",
            "    torch._C._cuda_init()\n",
            "RuntimeError: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python /content/Meta-Nerual-Trojan-Detection/model_lib/mnist_cnn_model.py"
      ],
      "metadata": {
        "id": "CCGQbHohXRfu"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cd .."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ESGYWdiKyoUU",
        "outputId": "15b05105-1fa9-4014-f929-d5849b4b2efc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cd Meta-Nerual-Trojan-Detection"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hmwhbUIeyyzK",
        "outputId": "d3cf1d7b-3783-41a8-cd39-7f5adce71e47"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/Meta-Nerual-Trojan-Detection\n"
          ]
        }
      ]
    }
  ]
}