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diff --git a/lab-isic/deepfake_detection_project/df_detect.ipynb b/lab-isic/deepfake_detection_project/df_detect.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "## DeepFake 检测\n",
+    "### 1. 解压数据集"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# 解压数据集到\"/home/aistudio/work/\"目录下\n",
+    "!unzip -d /home/aistudio/work/ /home/aistudio/data/data105515/dataset_video.zip"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### 2. 视频提帧"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "import cv2\n",
+    "\n",
+    "\n",
+    "def video_to_frames(source_path, target_dir, select_num, x):\n",
+    "    # select_num = 1, x = num; select_num = 0, x = interval\n",
+    "    cap = cv2.VideoCapture(source_path)\n",
+    "    if cap.isOpened():\n",
+    "        frame_num = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
+    "        # print(frame_num)\n",
+    "        if select_num == 1:\n",
+    "            num = int(x)\n",
+    "            if frame_num >= num:\n",
+    "                interval = frame_num//num\n",
+    "                # print(interval)\n",
+    "            else:\n",
+    "                # print('Error: expect num of frames > actual ...')\n",
+    "                os._exit(0)\n",
+    "        else:\n",
+    "            interval = int(x)\n",
+    "        v_name = os.path.split(source_path)[-1]\n",
+    "        v_name = v_name.split('.')[0]\n",
+    "        # print(v_name)\n",
+    "        for i in range(0, frame_num-1):\n",
+    "            ret, frame = cap.read()\n",
+    "            if ret:\n",
+    "                if i % interval == 0:\n",
+    "                    f_path = os.path.join(target_dir, v_name+'_'+str(i)+'.png')\n",
+    "                    print(f_path)\n",
+    "                    cv2.imwrite(f_path, frame)\n",
+    "    else:\n",
+    "        print('fail to read frame - video_to_face_frames')\n",
+    "        os._exit(0)\n",
+    "    cap.release()\n",
+    "\n",
+    "dataset_video = ['work/dataset_video/fake', 'work/dataset_video/real']\n",
+    "dataset_frame = ['work/dataset_frame/fake', 'work/dataset_frame/real']\n",
+    "\n",
+    "for i in range(2):\n",
+    "    for f in os.listdir(dataset_video[i]):\n",
+    "        # 每个视频取 10 帧\n",
+    "        video_to_frames(os.path.join(dataset_video[i], f), dataset_frame[i], 1, 10)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import PIL.Image as Image\n",
+    "\n",
+    "path='work/dataset_frame/fake/fake_127_252.png'\n",
+    "img = Image.open(path)\n",
+    "plt.imshow(img)\n",
+    "plt.show()               #显示 fake 帧\n",
+    "\n",
+    "path='work/dataset_frame/real/real_115_0.png'\n",
+    "img = Image.open(path)\n",
+    "plt.imshow(img)\n",
+    "plt.show()               #显示 real 帧"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### 3. 面部裁剪"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# 下载 pyramidbox_lite_mobile 预训练模型\n",
+    "!hub install pyramidbox_lite_mobile==1.2.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import paddlehub as hub\n",
+    "\n",
+    "import cv2\n",
+    "\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "face_detector = hub.Module(name=\"pyramidbox_lite_mobile\")\n",
+    "\n",
+    "frame_dir = ['work/dataset_frame/fake', 'work/dataset_frame/real']\n",
+    "face_dir = ['work/dataset_face/fake', 'work/dataset_face/real']\n",
+    "\n",
+    "for i in range(2):\n",
+    "    paths = os.listdir(frame_dir[i])\n",
+    "    paths = [os.path.join(frame_dir[i], file_name) for file_name in paths]\n",
+    "\n",
+    "    result = face_detector.face_detection(paths=paths)\n",
+    "    print(result[0])\n",
+    "\n",
+    "    for res in result:\n",
+    "        if [] != res['data']:\n",
+    "            # 扩大裁剪范围，尽可能包含五官和头发等\n",
+    "            x = (res['data'][0]['right'] - res['data'][0]['left']) // 5\n",
+    "            y = (res['data'][0]['bottom'] - res['data'][0]['top']) // 5\n",
+    "            l = res['data'][0]['left'] - x\n",
+    "            r = res['data'][0]['right'] + x\n",
+    "            t = res['data'][0]['top'] - y\n",
+    "            b = res['data'][0]['bottom'] + y\n",
+    "            im = cv2.imread(res['path'])\n",
+    "            im = im[t:b, l:r]\n",
+    "            _, s_name = os.path.split(res['path'])\n",
+    "            s_path = os.path.join(face_dir[i], s_name)\n",
+    "            cv2.imwrite(s_path, im)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# 删除空白样本(帧上未发现人脸)\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "face_dir = ['work/dataset_face/fake', 'work/dataset_face/real']\n",
+    "\n",
+    "for i in range(2):\n",
+    "    paths = os.listdir(face_dir[i])\n",
+    "    paths = [os.path.join(face_dir[i], file_name) for file_name in paths]\n",
+    "    \n",
+    "    for fpng in paths:\n",
+    "        if 0 == os.path.getsize(fpng):\n",
+    "            os.remove(fpng)\n",
+    "\n",
+    "print('fake 样本数：', len(os.listdir(face_dir[0])))\n",
+    "print('real 样本数：', len(os.listdir(face_dir[1])))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import PIL.Image as Image\n",
+    "\n",
+    "path='work/dataset_face/fake/fake_74_129.png'\n",
+    "img = Image.open(path)\n",
+    "plt.imshow(img)\n",
+    "plt.show()               #显示 fake 样本 \n",
+    "\n",
+    "path='work/dataset_face/real/real_58_150.png'\n",
+    "img = Image.open(path)\n",
+    "plt.imshow(img)\n",
+    "plt.show()               #显示 real 样本"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### 4. 自定义数据集"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "import paddle\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "import PIL.Image as Image\n",
+    "\n",
+    "from random import shuffle\n",
+    "\n",
+    "from paddle.io import Dataset\n",
+    "\n",
+    "from paddle.static import InputSpec\n",
+    "\n",
+    "from paddle.vision.models import resnet101\n",
+    "\n",
+    "from paddle.vision.transforms import Compose, Resize, Transpose, Normalize, RandomHorizontalFlip, RandomRotation\n",
+    "\n",
+    "\n",
+    "fake_list, real_list = [], []\n",
+    "fake_path = 'work/dataset_face/fake'\n",
+    "real_path = 'work/dataset_face/real'\n",
+    "\n",
+    "for root, dirs, files in os.walk(fake_path):\n",
+    "    for f in files:\n",
+    "        if f.endswith('png'):\n",
+    "            fake_list.append([os.path.join(fake_path, f), int(0)])\n",
+    "for root, dirs, files in os.walk(real_path):\n",
+    "    for f in files:\n",
+    "        if f.endswith('png'):\n",
+    "            real_list.append([os.path.join(real_path, f), int(1)])\n",
+    "\n",
+    "print(len(fake_list))\n",
+    "print(fake_list[0])\n",
+    "print(len(real_list))\n",
+    "print(real_list[0])\n",
+    "\n",
+    "data_list = fake_list + real_list\n",
+    "shuffle(data_list)\n",
+    "\n",
+    "def preprocess(img):\n",
+    "    #预处理\n",
+    "    transform = Compose([\n",
+    "        Resize(size=(224,224)),\n",
+    "        Normalize([0.5] * 3, [0.5] * 3, data_format='HWC'),\n",
+    "        RandomHorizontalFlip(0.5),\n",
+    "        RandomRotation(0.5),\n",
+    "        Transpose(),\n",
+    "        ])\n",
+    "    img = transform(img).astype(\"float32\")\n",
+    "    return img\n",
+    "\n",
+    "class dfdcDataset(Dataset):\n",
+    "    #自定义dfdc数据集\n",
+    "    def __init__(self, data, is_val=False):\n",
+    "        super().__init__()\n",
+    "        #取20%的样本作为测试集，80%的样本作为训练集。\n",
+    "        self.samples = data[-int(len(data)*0.3):] if is_val else data[:-int(len(data)*0.3)]\n",
+    "\n",
+    "    def __getitem__(self, idx):\n",
+    "        #处理图像\n",
+    "        img_path = self.samples[idx][0] #得到某样本的路径\n",
+    "        #img = np.array(Image.open(img_path))\n",
+    "        img = Image.open(img_path)\n",
+    "        if img.mode != 'RGB':\n",
+    "            img = img.convert('RGB')\n",
+    "        img = preprocess(img)\n",
+    "\n",
+    "        #处理标签\n",
+    "        label = self.samples[idx][1] #得到某样本的标签\n",
+    "        label = np.array([label], dtype=\"int64\") #把标签数据类型转成int64\n",
+    "        return img, label\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        #返回每个Epoch中图片数量\n",
+    "        return len(self.samples)\n",
+    "\n",
+    "train_set = dfdcDataset(data_list, is_val=False)\n",
+    "eval_set = dfdcDataset(data_list, is_val=True)\n",
+    "\n",
+    "print(train_set[0][0].shape)\n",
+    "print(train_set[0][1].shape)\n",
+    "print(len(train_set))\n",
+    "print(len(eval_set))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### 5. 训练模型"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "选择 ResNet101 网络"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# build model \n",
+    "net = resnet101(pretrained=True)\n",
+    "net.fc = paddle.nn.Linear(2048, 2)\n",
+    "# 打印网络结构\n",
+    "net"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "inputs = InputSpec([None, 3, 224, 224], 'float32', 'x')\n",
+    "labels = InputSpec([None, 1], 'int64', 'label')\n",
+    "\n",
+    "model=paddle.Model(net, inputs, labels)\n",
+    "\n",
+    "model.summary((1,3,224,224))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "模型训练"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "model.prepare(optimizer=paddle.optimizer.Adam(learning_rate=0.001,parameters=model.parameters()),\n",
+    "              loss=paddle.nn.CrossEntropyLoss(),\n",
+    "              metrics=paddle.metric.Accuracy())\n",
+    "\n",
+    "\n",
+    "model.fit(\n",
+    "    train_data=train_set, \n",
+    "    eval_data=eval_set, \n",
+    "    batch_size=64, \n",
+    "    epochs=20, \n",
+    "    verbose=1, \n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "请点击[此处](https://ai.baidu.com/docs#/AIStudio_Project_Notebook/a38e5576)查看本环境基本用法.  <br>\n",
+    "Please click [here ](https://ai.baidu.com/docs#/AIStudio_Project_Notebook/a38e5576) for more detailed instructions. "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "PaddlePaddle 2.1.2 (Python 3.5)",
+   "language": "python",
+   "name": "py35-paddle1.2.0"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}