├── README.md ├── picture ├── 120.jpg ├── 40.jpeg ├── 60.jpeg ├── Original_Traffic_Sign_samples │ ├── 01_speed_limit_30.jpg │ ├── 02_no_entry.png │ ├── 03_turn_right.png │ ├── 04_no_entry.jpg │ ├── 05_no_vehicles.jpg │ ├── 06_speed_limit_60.png │ ├── 07_keep_left.png │ ├── 08_vehicle_prohibited.png │ ├── 09_speed_limit_30.jpg │ ├── 10_speed_limit_100.jpg │ ├── 11_speed_limit_60.jpg │ ├── 12_speed_limit_100.jpg │ ├── 13_no_passing.jpg │ ├── 14_stop.png │ ├── 15_stop.jpg │ ├── 16_roundabout_mandatory.png │ ├── 17_no_passing_big.png │ ├── 18_general_caution.png │ ├── 19_rightway.png │ ├── 20_speed_limit_50.png │ ├── 21_dangerous_curve_right.png │ ├── 22_end.png │ ├── 23_speed_limit_120.jpg │ ├── 24_speed_limit_80.jpg │ ├── 25_straight.png │ ├── 26_bicycles_crossing.png │ ├── 27_end_passing.png │ └── labels.txt ├── bigpig.jpeg ├── book5-home.graffle │ ├── data.plist │ ├── image1.png │ ├── image2.pdf │ ├── image3.png │ ├── image4.png │ └── image5.png ├── book5picture.graffle │ ├── data.plist │ ├── image1.png │ ├── image10.png │ ├── image2.tiff │ ├── image3.png │ ├── image4.png │ ├── image6.png │ ├── image7.pdf │ └── image8.png ├── cow.jpeg ├── cropped_panda.jpg ├── deepfool算法.png ├── hacked-pig-image-deepfool-d.png ├── hacked-pig-image-deepfool.png ├── hacked-pig-image-fgsm-d.png ├── hacked-pig-image-fgsm.png ├── hacked-pig-image.png ├── horse.jpeg ├── left.jpg ├── lena.jpeg ├── mug227.png ├── objectdetect │ ├── adv-case1.jpg │ ├── adv-case2.jpg │ ├── case1.jpeg │ ├── case2.jpeg │ ├── case3.jpeg │ ├── ssd-case1.jpeg │ ├── ssd-case2.jpeg │ ├── ssd-case3.jpeg │ └── ssd-case3.jpg ├── pig.jpg ├── road.jpg ├── sigmod函数.png ├── sign.jpeg ├── sign函数.png ├── smallpig.jpeg ├── sobel.jpeg ├── test_im1.jpg ├── test_im2.jpg ├── traffic_sign_samples │ ├── 0000.jpg │ ├── 0001.jpg │ ├── 0002.jpg │ ├── 0003.png │ ├── 0004.jpeg │ ├── 0005.jpg │ ├── 0006.jpg │ ├── 0006.png │ ├── 0007.jpg │ ├── 0008.jpg │ ├── 0009.jpg │ ├── 0010.jpg │ ├── 0011.jpg │ ├── 0012.jpg │ ├── 0013.jpg │ ├── 0014.jpg │ ├── 0015.jpg │ ├── 0016.jpg │ ├── 0017.jpg │ ├── 0018.jpg │ ├── 0019.jpg │ ├── 0020.jpg │ ├── 0021.jpg │ ├── 0022.jpg │ ├── 0023.jpg │ ├── 0024.jpg │ ├── 0025.jpg │ ├── 0026.jpg │ ├── 0027.jpg │ ├── 0028.jpg │ ├── 0029.jpg │ └── 0030.jpg ├── v.mp4 ├── 下载.png ├── 二分类问题里面的对抗样本.png ├── 作图.graffle ├── 分类问题原理图1.png ├── 分类问题原理图2.png ├── 向量化表示的点到直线的距离.png ├── 基于FGSM算法被识别为烤面包机的家猪-图1.png ├── 基于FGSM算法被识别为烤面包机的家猪-图2.png ├── 多分类问题的deepfool.png ├── 攻击图片经过拍照打印后依然可以欺骗图像分类模型.png ├── 点到直线的距离.png ├── 神经网络训练过程.graffle ├── 神经网络训练过程.png └── 针对图像分类模型的对抗样本.png ├── 白盒 ├── LocalSearchAttack │ ├── .ipynb_checkpoints │ │ └── LocalSearch-checkpoint.ipynb │ └── LocalSearch.ipynb ├── Universal │ ├── .ipynb_checkpoints │ │ └── UniversalAttack-checkpoint.ipynb │ ├── MNIST_data │ │ ├── t10k-images-idx3-ubyte │ │ ├── t10k-labels-idx1-ubyte │ │ ├── train-images-idx3-ubyte │ │ └── train-labels-idx1-ubyte │ └── UniversalAttack.ipynb ├── UniversalAttack │ ├── .gitignore │ ├── DeepFool │ │ └── Python │ │ │ ├── README.md │ │ │ └── deepfool.py │ ├── LICENSE │ ├── README.md │ ├── attack.ipynb │ ├── attack.py │ ├── data │ │ ├── dog.556.jpg │ │ ├── dog.557.jpg │ │ ├── dog.559.jpg │ │ ├── dog.560.jpg │ │ ├── dog.562.jpg │ │ └── dog.563.jpg │ ├── noise.pth │ └── uap.ipynb └── 单像素攻击 │ ├── .ipynb_checkpoints │ └── one-pixel-attack-checkpoint.ipynb │ ├── one-pixel-attack.ipynb │ └── runs │ └── Aug05_19-22-30_t630 │ └── events.out.tfevents.1565004150.t630 └── 黑盒 ├── CW ├── .ipynb_checkpoints │ └── CW-checkpoint.ipynb └── CW.ipynb ├── DeepFool ├── .ipynb_checkpoints │ ├── Notarget-checkpoint.ipynb │ └── Target-checkpoint.ipynb ├── Notarget.ipynb └── Target.ipynb ├── FGM ├── .ipynb_checkpoints │ └── FGM-checkpoint.ipynb └── FGM.ipynb ├── JSMA ├── .ipynb_checkpoints │ └── JSMA-checkpoint.ipynb └── JSMA.ipynb └── 梯度方向 ├── .ipynb_checkpoints └── 梯度方向-checkpoint.ipynb └── 梯度方向.ipynb /README.md: -------------------------------------------------------------------------------- 1 | # Adversarial-Examples 2 | AdvAttacks; adversarial examples; FGSM;JSMA;CW;single pixel attack; local search attack;deepfool 3 | 4 | 根据根据成本，分为白盒，黑盒，物理攻击。 白盒和黑盒文件夹标记反了 5 | -------------------------------------------------------------------------------- /picture/120.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/FenHua/Adversarial-Examples/d8479386e041ce7a73f9cf353b5923ec3f274526/picture/120.jpg -------------------------------------------------------------------------------- /picture/40.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/FenHua/Adversarial-Examples/d8479386e041ce7a73f9cf353b5923ec3f274526/picture/40.jpeg -------------------------------------------------------------------------------- /picture/60.jpeg: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import foolbox\n", 10 | "import keras\n", 11 | "import numpy as np\n", 12 | "from keras.applications.resnet50 import ResNet50,decode_predictions\n", 13 | "from foolbox.criteria import TargetClassProbability\n", 14 | "'''\n", 15 | "keras.backend.set_learning_pharse(0) # train model\n", 16 | "keras.backend.set_learning_pharse(1) # predict model\n", 17 | "之所以这么区分，是因为某些层在预测和训练时不同，比如BN和Dropout\n", 18 | "'''\n", 19 | "keras.backend.set_learning_phase(1)\n", 20 | "kmodel=ResNet50(weights='imagenet')\n", 21 | "preprocessing=(np.array([104,116,123]),1)\n", 22 | "fmodel=foolbox.models.KerasModel(kmodel,bounds=(0,255),preprocessing=preprocessing)\n", 23 | "# get source image and label\n", 24 | "image,label = foolbox.utils.imagenet_example()\n", 25 | "attack=foolbox.attacks.LocalSearchAttack(fmodel)\n", 26 | "adversarial = attack(image[:,:,:,-1],label) # if the attack fails,adversarial\n", 27 | "# will be None and a warning will be printed\n", 28 | "if adversarial is not None:\n", 29 | " adversarial_label=np.argmax(fmodel.predictions(adversarial))\n", 30 | " print('Adversarial label={}, Original label={}'.format(adversarial_label,label))\n", 31 | "else:\n", 32 | " print('Fail to LocalSearchAttack!')" 33 | ] 34 | }, 35 | { 36 | "cell_type": "code", 37 | "execution_count": null, 38 | "metadata": { 39 | "collapsed": true 40 | }, 41 | "outputs": [], 42 | "source": [ 43 | "%matplotlip inline\n", 44 | "import matplotlip.pyplot as plt\n", 45 | "plt.figure()\n", 46 | "plt.subplot(131)\n", 47 | "plt.title('Original')\n", 48 | "plt.imshow(image/255) # division by 255 to convert [0,255] to [0,1]\n", 49 | "plt.axis('off')\n", 50 | "\n", 51 | "plt.subplot(132)\n", 52 | "plt.title('Adversarial')\n", 53 | "plt.imshow(adversarial[:,:,::-1]/255) #::-1 to convert BGR to RGB\n", 54 | "plt.axis('off')\n", 55 | "\n", 56 | "plt.subplot(133)\n", 57 | "plt.title('Difference')\n", 58 | "difference=adversarial[:,:,::-1]-image\n", 59 | "plt.image(diffenrence/abs(difference).max() *0.2+0.5)\n", 60 | "plt.axis('off')\n", 61 | "plt.show()" 62 | ] 63 | }, 64 | { 65 | "cell_type": "code", 66 | "execution_count": null, 67 | "metadata": { 68 | "collapsed": true 69 | }, 70 | "outputs": [], 71 | "source": [ 72 | "print(np.where(difference!=0.0))" 73 | ] 74 | } 75 | ], 76 | "metadata": { 77 | "kernelspec": { 78 | "display_name": "Python 3", 79 | "language": "python", 80 | "name": "python3" 81 | }, 82 | "language_info": { 83 | "codemirror_mode": { 84 | "name": "ipython", 85 | "version": 3 86 | }, 87 | "file_extension": ".py", 88 | "mimetype": "text/x-python", 89 | "name": "python", 90 | "nbconvert_exporter": "python", 91 | "pygments_lexer": "ipython3", 92 | "version": "3.6.2" 93 | } 94 | }, 95 | "nbformat": 4, 96 | "nbformat_minor": 2 97 | } 98 | -------------------------------------------------------------------------------- /白盒/LocalSearchAttack/LocalSearch.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import foolbox\n", 10 | "import keras\n", 11 | "import numpy as np\n", 12 | "from keras.applications.resnet50 import ResNet50,decode_predictions\n", 13 | "from foolbox.criteria import TargetClassProbability\n", 14 | "'''\n", 15 | "keras.backend.set_learning_pharse(0) # train model\n", 16 | "keras.backend.set_learning_pharse(1) # predict model\n", 17 | "之所以这么区分，是因为某些层在预测和训练时不同，比如BN和Dropout\n", 18 | "'''\n", 19 | "keras.backend.set_learning_phase(1)\n", 20 | "kmodel=ResNet50(weights='imagenet')\n", 21 | "preprocessing=(np.array([104,116,123]),1)\n", 22 | "fmodel=foolbox.models.KerasModel(kmodel,bounds=(0,255),preprocessing=preprocessing)\n", 23 | "# get source image and label\n", 24 | "image,label = foolbox.utils.imagenet_example()\n", 25 | "attack=foolbox.attacks.LocalSearchAttack(fmodel)\n", 26 | "adversarial = attack(image[:,:,:,-1],label) # if the attack fails,adversarial\n", 27 | "# will be None and a warning will be printed\n", 28 | "if adversarial is not None:\n", 29 | " adversarial_label=np.argmax(fmodel.predictions(adversarial))\n", 30 | " print('Adversarial label={}, Original label={}'.format(adversarial_label,label))\n", 31 | "else:\n", 32 | " print('Fail to LocalSearchAttack!')" 33 | ] 34 | }, 35 | { 36 | "cell_type": "code", 37 | "execution_count": null, 38 | "metadata": { 39 | "collapsed": true 40 | }, 41 | "outputs": [], 42 | "source": [ 43 | "%matplotlip inline\n", 44 | "import matplotlip.pyplot as plt\n", 45 | "plt.figure()\n", 46 | "plt.subplot(131)\n", 47 | "plt.title('Original')\n", 48 | "plt.imshow(image/255) # division by 255 to convert [0,255] to [0,1]\n", 49 | "plt.axis('off')\n", 50 | "\n", 51 | "plt.subplot(132)\n", 52 | "plt.title('Adversarial')\n", 53 | "plt.imshow(adversarial[:,:,::-1]/255) #::-1 to convert BGR to RGB\n", 54 | "plt.axis('off')\n", 55 | "\n", 56 | "plt.subplot(133)\n", 57 | "plt.title('Difference')\n", 58 | "difference=adversarial[:,:,::-1]-image\n", 59 | "plt.image(diffenrence/abs(difference).max() *0.2+0.5)\n", 60 | "plt.axis('off')\n", 61 | "plt.show()" 62 | ] 63 | }, 64 | { 65 | "cell_type": "code", 66 | "execution_count": null, 67 | "metadata": { 68 | "collapsed": true 69 | }, 70 | "outputs": [], 71 | "source": [ 72 | "print(np.where(difference!=0.0))" 73 | ] 74 | } 75 | ], 76 | "metadata": { 77 | "kernelspec": { 78 | "display_name": "Python 3", 79 | "language": "python", 80 | "name": "python3" 81 | }, 82 | "language_info": { 83 | "codemirror_mode": { 84 | "name": "ipython", 85 | "version": 3 86 | }, 87 | "file_extension": ".py", 88 | "mimetype": "text/x-python", 89 | "name": "python", 90 | "nbconvert_exporter": "python", 91 | "pygments_lexer": "ipython3", 92 | "version": "3.6.2" 93 | } 94 | }, 95 | "nbformat": 4, 96 | "nbformat_minor": 2 97 | } 98 | -------------------------------------------------------------------------------- /白盒/Universal/.ipynb_checkpoints/UniversalAttack-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import tensorflow as tf\n", 10 | "import numpy as np\n", 11 | "from tensorflow.examples.tutorials.mnist import input_data\n", 12 | "from tensorflow.python.framework import graph_util\n", 13 | "from tensorflow.python.platform import gfile\n", 14 | "import os\n", 15 | "pb_file_path=\"tensorflow_mnist_graph.pb\" # 模型文件\n", 16 | "mnist=input_data.read_data_sets('MNIST_data/',one_hot=True) # mnist 数据\n", 17 | "# 预测用的会话\n", 18 | "persisted_sess = tf.Session()\n", 19 | "with gfile.FastGFile(pb_file_path,'rb') as f:\n", 20 | " graph_def=tf.GraphDef()\n", 21 | " graph_def.ParseFromString(f.read())\n", 22 | " persisted_sess.graph.as_default()\n", 23 | " tf.import_graph_def(graph_def,name='')\n", 24 | "persisted_sess.graph.get_operations()\n", 25 | "# 记载输入和输出tensor\n", 26 | "persisted_input=persisted_sess.graph.get_tensor_by_name(\"input:0\")\n", 27 | "persisted_keep_prob = persisted_sess.graph.get_tensor_by_name(\"keep_prob:0\")\n", 28 | "persisted_output = persisted_sess.graph.get_tensor_by_name(\"output:0\")" 29 | ] 30 | }, 31 | { 32 | "cell_type": "code", 33 | "execution_count": null, 34 | "metadata": { 35 | "collapsed": true 36 | }, 37 | "outputs": [], 38 | "source": [ 39 | "print('>> Computing feedforward function...')\n", 40 | "def f(image_inp,keep_prob=1.0):\n", 41 | " return persisted_sess.run(persisted_output, \n", 42 | " feed_dict={persisted_input: image_inp,persisted_keep_prob:keep_prob})\n", 43 | "# 数据已经归一化\n", 44 | "test_x=mnist.test.images\n", 45 | "test_y=mnist.test.labels" 46 | ] 47 | }, 48 | { 49 | "cell_type": "code", 50 | "execution_count": null, 51 | "metadata": { 52 | "collapsed": true 53 | }, 54 | "outputs": [], 55 | "source": [ 56 | "# 批量验证测试集的准确率\n", 57 | "from sklearn.metrics import accuracy_score\n", 58 | "y_pred=np.argmax(f(test_x),axis=1)\n", 59 | "y_true=np.argmax(test_y,axis=1)\n", 60 | "print(accuracy_score(y_true,y_pred,normalize=True))\n", 61 | "num_images=test_x.shape[0]\n", 62 | "fooling_rate=float(np.sum(y_pred!=y_true)/float(num_images))\n", 63 | "print('FOOLING RATE = ',fooling_rate)" 64 | ] 65 | }, 66 | { 67 | "cell_type": "code", 68 | "execution_count": null, 69 | "metadata": { 70 | "collapsed": true 71 | }, 72 | "outputs": [], 73 | "source": [ 74 | "def jacobian(y_flat,x,inds):\n", 75 | " n=10\n", 76 | " loop_vars=[\n", 77 | " tf.constant(0,tf.int32),\n", 78 | " tf.TensorArray(tf.float32,size=n)\n", 79 | " ]\n", 80 | " _,jocabian=tf.while_loop(\n", 81 | " lambda j,_:j> Computing gradient function...')\n", 198 | "def grad_fs(image_inp, indices,keep_prob=1.0): \n", 199 | " return persisted_sess.run(dydx, feed_dict={persisted_input: image_inp, inds: indices,persisted_keep_prob:keep_prob}).squeeze(axis=1)\n", 200 | "\n", 201 | "# Running universal perturbation\n", 202 | "v = universal_perturbation(X, f, grad_fs, delta=0.2,num_classes=10)" 203 | ] 204 | }, 205 | { 206 | "cell_type": "code", 207 | "execution_count": null, 208 | "metadata": { 209 | "collapsed": true 210 | }, 211 | "outputs": [], 212 | "source": [ 213 | "y_pred=np.argmax(f(test_x+v),axis=1)\n", 214 | "y_true=np.argmax(test_y,axis=1)\n", 215 | "\n", 216 | "print(accuracy_score(y_true, y_pred, normalize=True))\n", 217 | "\n", 218 | "num_images=test_x.shape[0]\n", 219 | "\n", 220 | "fooling_rate = float(np.sum(y_pred != y_true) / float(num_images))\n", 221 | "print('FOOLING RATE = ', fooling_rate)" 222 | ] 223 | } 224 | ], 225 | "metadata": { 226 | "kernelspec": { 227 | "display_name": "Python 3", 228 | "language": "python", 229 | "name": "python3" 230 | }, 231 | "language_info": { 232 | "codemirror_mode": { 233 | "name": "ipython", 234 | "version": 3 235 | }, 236 | "file_extension": ".py", 237 | "mimetype": "text/x-python", 238 | "name": "python", 239 | "nbconvert_exporter": "python", 240 | "pygments_lexer": "ipython3", 241 | "version": "3.6.2" 242 | } 243 | }, 244 | "nbformat": 4, 245 | "nbformat_minor": 2 246 | } 247 | -------------------------------------------------------------------------------- /白盒/Universal/MNIST_data/t10k-images-idx3-ubyte: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/FenHua/Adversarial-Examples/d8479386e041ce7a73f9cf353b5923ec3f274526/白盒/Universal/MNIST_data/t10k-images-idx3-ubyte -------------------------------------------------------------------------------- /白盒/Universal/MNIST_data/t10k-labels-idx1-ubyte: -------------------------------------------------------------------------------- 1 | '                                                    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-------------------------------------------------------------------------------- https://raw.githubusercontent.com/FenHua/Adversarial-Examples/d8479386e041ce7a73f9cf353b5923ec3f274526/白盒/Universal/MNIST_data/train-images-idx3-ubyte -------------------------------------------------------------------------------- /白盒/Universal/MNIST_data/train-labels-idx1-ubyte: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/FenHua/Adversarial-Examples/d8479386e041ce7a73f9cf353b5923ec3f274526/白盒/Universal/MNIST_data/train-labels-idx1-ubyte -------------------------------------------------------------------------------- /白盒/Universal/UniversalAttack.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import tensorflow as tf\n", 10 | "import numpy as np\n", 11 | "from tensorflow.examples.tutorials.mnist import input_data\n", 12 | "from tensorflow.python.framework import graph_util\n", 13 | "from tensorflow.python.platform import gfile\n", 14 | "import os\n", 15 | "pb_file_path=\"tensorflow_mnist_graph.pb\" # 模型文件\n", 16 | "mnist=input_data.read_data_sets('MNIST_data/',one_hot=True) # mnist 数据\n", 17 | "# 预测用的会话\n", 18 | "persisted_sess = tf.Session()\n", 19 | "with gfile.FastGFile(pb_file_path,'rb') as f:\n", 20 | " graph_def=tf.GraphDef()\n", 21 | " graph_def.ParseFromString(f.read())\n", 22 | " persisted_sess.graph.as_default()\n", 23 | " tf.import_graph_def(graph_def,name='')\n", 24 | "persisted_sess.graph.get_operations()\n", 25 | "# 记载输入和输出tensor\n", 26 | "persisted_input=persisted_sess.graph.get_tensor_by_name(\"input:0\")\n", 27 | "persisted_keep_prob = persisted_sess.graph.get_tensor_by_name(\"keep_prob:0\")\n", 28 | "persisted_output = persisted_sess.graph.get_tensor_by_name(\"output:0\")" 29 | ] 30 | }, 31 | { 32 | "cell_type": "code", 33 | "execution_count": null, 34 | "metadata": { 35 | "collapsed": true 36 | }, 37 | "outputs": [], 38 | "source": [ 39 | "print('>> Computing feedforward function...')\n", 40 | "def f(image_inp,keep_prob=1.0):\n", 41 | " return persisted_sess.run(persisted_output, \n", 42 | " feed_dict={persisted_input: image_inp,persisted_keep_prob:keep_prob})\n", 43 | "# 数据已经归一化\n", 44 | "test_x=mnist.test.images\n", 45 | "test_y=mnist.test.labels" 46 | ] 47 | }, 48 | { 49 | "cell_type": "code", 50 | "execution_count": null, 51 | "metadata": { 52 | "collapsed": true 53 | }, 54 | "outputs": [], 55 | "source": [ 56 | "# 批量验证测试集的准确率\n", 57 | "from sklearn.metrics import accuracy_score\n", 58 | "y_pred=np.argmax(f(test_x),axis=1)\n", 59 | "y_true=np.argmax(test_y,axis=1)\n", 60 | "print(accuracy_score(y_true,y_pred,normalize=True))\n", 61 | "num_images=test_x.shape[0]\n", 62 | "fooling_rate=float(np.sum(y_pred!=y_true)/float(num_images))\n", 63 | "print('FOOLING RATE = ',fooling_rate)" 64 | ] 65 | }, 66 | { 67 | "cell_type": "code", 68 | "execution_count": null, 69 | "metadata": { 70 | "collapsed": true 71 | }, 72 | "outputs": [], 73 | "source": [ 74 | "def jacobian(y_flat,x,inds):\n", 75 | " n=10\n", 76 | " loop_vars=[\n", 77 | " tf.constant(0,tf.int32),\n", 78 | " tf.TensorArray(tf.float32,size=n)\n", 79 | " ]\n", 80 | " _,jocabian=tf.while_loop(\n", 81 | " lambda j,_:j> Computing gradient function...')\n", 198 | "def grad_fs(image_inp, indices,keep_prob=1.0): \n", 199 | " return persisted_sess.run(dydx, feed_dict={persisted_input: image_inp, inds: indices,persisted_keep_prob:keep_prob}).squeeze(axis=1)\n", 200 | "\n", 201 | "# Running universal perturbation\n", 202 | "v = universal_perturbation(X, f, grad_fs, delta=0.2,num_classes=10)" 203 | ] 204 | }, 205 | { 206 | "cell_type": "code", 207 | "execution_count": null, 208 | "metadata": { 209 | "collapsed": true 210 | }, 211 | "outputs": [], 212 | "source": [ 213 | "y_pred=np.argmax(f(test_x+v),axis=1)\n", 214 | "y_true=np.argmax(test_y,axis=1)\n", 215 | "\n", 216 | "print(accuracy_score(y_true, y_pred, normalize=True))\n", 217 | "\n", 218 | "num_images=test_x.shape[0]\n", 219 | "\n", 220 | "fooling_rate = float(np.sum(y_pred != y_true) / float(num_images))\n", 221 | "print('FOOLING RATE = ', fooling_rate)" 222 | ] 223 | } 224 | ], 225 | "metadata": { 226 | "kernelspec": { 227 | "display_name": "Python 3", 228 | "language": "python", 229 | "name": "python3" 230 | }, 231 | "language_info": { 232 | "codemirror_mode": { 233 | "name": "ipython", 234 | "version": 3 235 | }, 236 | "file_extension": ".py", 237 | "mimetype": "text/x-python", 238 | "name": "python", 239 | "nbconvert_exporter": "python", 240 | "pygments_lexer": "ipython3", 241 | "version": "3.6.2" 242 | } 243 | }, 244 | "nbformat": 4, 245 | "nbformat_minor": 2 246 | } 247 | -------------------------------------------------------------------------------- /白盒/UniversalAttack/.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | MANIFEST 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | .pytest_cache/ 49 | 50 | # Translations 51 | *.mo 52 | *.pot 53 | 54 | # Django stuff: 55 | *.log 56 | local_settings.py 57 | db.sqlite3 58 | 59 | # Flask stuff: 60 | instance/ 61 | .webassets-cache 62 | 63 | # Scrapy stuff: 64 | .scrapy 65 | 66 | # Sphinx documentation 67 | docs/_build/ 68 | 69 | # PyBuilder 70 | target/ 71 | 72 | # Jupyter Notebook 73 | .ipynb_checkpoints 74 | 75 | # pyenv 76 | .python-version 77 | 78 | # celery beat schedule file 79 | celerybeat-schedule 80 | 81 | # SageMath parsed files 82 | *.sage.py 83 | 84 | # Environments 85 | .env 86 | .venv 87 | env/ 88 | venv/ 89 | ENV/ 90 | env.bak/ 91 | venv.bak/ 92 | 93 | # Spyder project settings 94 | .spyderproject 95 | .spyproject 96 | 97 | # Rope project settings 98 | .ropeproject 99 | 100 | # mkdocs documentation 101 | /site 102 | 103 | # mypy 104 | .mypy_cache/ 105 | 106 | # data 107 | running_images/ 108 | -------------------------------------------------------------------------------- /白盒/UniversalAttack/DeepFool/Python/README.md: -------------------------------------------------------------------------------- 1 | # DeepFool 2 | DeepFool is a simple algorithm to find the minimum adversarial perturbations in deep networks 3 | 4 | ### deepfool.py 5 | 6 | This function implements the algorithm proposed in [[1]](http://arxiv.org/pdf/1511.04599) using PyTorch to find adversarial perturbations. 7 | 8 | __Note__: The final softmax (loss) layer should be removed in order to prevent numerical instabilities. 9 | 10 | The parameters of the function are: 11 | 12 | - `image`: Image of size `HxWx3d` 13 | - `net`: neural network (input: images, output: values of activation **BEFORE** softmax). 14 | - `num_classes`: limits the number of classes to test against, by default = 10. 15 | - `max_iter`: max number of iterations, by default = 50. 16 | 17 | ### test_deepfool.py 18 | 19 | A simple demo which computes the adversarial perturbation for a test image from ImageNet dataset. 20 | 21 | ## Reference 22 | [1] S. Moosavi-Dezfooli, A. Fawzi, P. Frossard: 23 | *DeepFool: a simple and accurate method to fool deep neural networks*. In Computer Vision and Pattern Recognition (CVPR ’16), IEEE, 2016. 24 | -------------------------------------------------------------------------------- /白盒/UniversalAttack/DeepFool/Python/deepfool.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from torch.autograd import Variable 3 | import torch as torch 4 | import copy 5 | from torch.autograd.gradcheck import zero_gradients 6 | 7 | 8 | def deepfool(image, net, device, num_classes=10, overshoot=0.02, max_iter=50): 9 | 10 | """ 11 | :param image: Image of size HxWx3 12 | :param net: network (input: images, output: values of activation **BEFORE** softmax). 13 | :param num_classes: num_classes (limits the number of classes to test against, by default = 10) 14 | :param overshoot: used as a termination criterion to prevent vanishing updates (default = 0.02). 15 | :param max_iter: maximum number of iterations for deepfool (default = 50) 16 | :return: minimal perturbation that fools the classifier, number of iterations that it required, new estimated_label and perturbed image 17 | """ 18 | image = image.to(device) 19 | net = net.to(device) 20 | 21 | f_image = net.forward(image.unsqueeze(0)).detach().cpu().numpy().flatten() 22 | I = (np.array(f_image)).flatten().argsort()[::-1] 23 | 24 | I = I[0:num_classes] 25 | label = I[0] 26 | 27 | input_shape = image.shape 28 | pert_image = image 29 | 30 | w = np.zeros(input_shape) 31 | r_tot = np.zeros(input_shape) 32 | 33 | loop_i = 0 34 | 35 | x = pert_image.unsqueeze(0) 36 | x.requires_grad_() 37 | 38 | fs = net.forward(x) 39 | fs_list = [fs[0,I[k]] for k in range(num_classes)] 40 | k_i = label 41 | 42 | while k_i == label and loop_i < max_iter: 43 | 44 | pert = np.inf 45 | fs[0, I[0]].backward(retain_graph=True) 46 | grad_orig = x.grad.data.cpu().numpy().copy() 47 | 48 | for k in range(1, num_classes): 49 | zero_gradients(x) 50 | 51 | fs[0, I[k]].backward(retain_graph=True) 52 | cur_grad = x.grad.data.cpu().numpy().copy() 53 | 54 | # set new w_k and new f_k 55 | w_k = cur_grad - grad_orig 56 | f_k = (fs[0, I[k]] - fs[0, I[0]]).data.cpu().numpy() 57 | 58 | pert_k = abs(f_k)/np.linalg.norm(w_k.flatten()) 59 | 60 | # determine which w_k to use 61 | if pert_k < pert: 62 | pert = pert_k 63 | w = w_k 64 | 65 | # compute r_i and r_tot 66 | r_i = pert * w / np.linalg.norm(w) 67 | r_tot = np.float32(r_tot + r_i) 68 | 69 | pert_image = image + (1+overshoot)*torch.from_numpy(r_tot).to(device) 70 | 71 | x = pert_image 72 | x.requires_grad_() 73 | fs=net.forward(x) 74 | k_i = np.argmax(fs.data.cpu().numpy().flatten()) 75 | 76 | loop_i += 1 77 | 78 | r_tot = (1+overshoot)*r_tot 79 | 80 | return r_tot, loop_i, label, k_i, pert_image -------------------------------------------------------------------------------- /白盒/UniversalAttack/LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /白盒/UniversalAttack/README.md: -------------------------------------------------------------------------------- 1 | # Universal_Adversarial_Perturbation_pytorch 2 | A PyTorch Implementation of the UAP attack 3 | 4 | [Universal Adversarial Perturbation](https://arxiv.org/pdf/1610.08401.pdf): Moosavi-Dezfooli, Seyed-Mohsen, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard. "Universal adversarial perturbations." arXiv preprint (2017). 5 | 6 | DeepFool implementation adapted from: [LTS4/DeepFool](https://github.com/LTS4/DeepFool/tree/master/Python) 7 | -------------------------------------------------------------------------------- /白盒/UniversalAttack/attack.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import time\n", 12 | "import random\n", 13 | "import numpy as np\n", 14 | "import torch.nn as nn\n", 15 | "from PIL import Image\n", 16 | "import torchvision.models as models\n", 17 | "import torchvision.transforms as transforms\n", 18 | "from DeepFool.Python.deepfool import deepfool\n", 19 | "from torch.autograd.gradcheck import zero_gradients\n", 20 | "def get_model(device):\n", 21 | " net=models.vgg16(pretrained=True)\n", 22 | " net.eval()\n", 23 | " net=net.to(device)\n", 24 | " return net\n", 25 | "def data_input_init(xi):\n", 26 | " mean = [ 0.485, 0.456, 0.406 ]\n", 27 | " std = [ 0.229, 0.224, 0.225 ]\n", 28 | " transform = transforms.Compose([\n", 29 | " transforms.Resize(256),\n", 30 | " transforms.CenterCrop(224),\n", 31 | " transforms.ToTensor(),\n", 32 | " transforms.Normalize(mean = mean,\n", 33 | " std = std)])\n", 34 | " return (mean,std,transform)\n", 35 | "def proj_lp(v,xi,p):\n", 36 | " # 映射到p阶范式球形上，球的中心是0，半径为xi\n", 37 | " if p==np.inf:\n", 38 | " v=torch.clamp(v,-xi,xi) # 无穷范式\n", 39 | " else:\n", 40 | " v=v*min(1,xi/(torch.norm(v,p)+0.00001))\n", 41 | " return v\n", 42 | "def get_fooling_rate(data_list,v,model,device):\n", 43 | " # data_list 输入图片的路经\n", 44 | " # v: Noise matrix\n", 45 | " # model: 目标网络\n", 46 | " # device: 计算设备\n", 47 | " tf = data_input_init(0)[2]\n", 48 | " num_images = len(data_list)\n", 49 | " fooled=0.0\n", 50 | " for name in tqdm(data_list):\n", 51 | " image = Image.open(name)\n", 52 | " image =tf(image)\n", 53 | " image=image.unsqueeze(0)\n", 54 | " image=image.to(device)\n", 55 | " _,pred = torch.max(model(image),1)\n", 56 | " _,adv_pred = torch.max(model(image+v),1)\n", 57 | " if(pred != adv_pred):\n", 58 | " fooled+=1\n", 59 | " # 计算愚弄成功率\n", 60 | " fooling_rate = fooled/num_images\n", 61 | " print('Fooling Rate = ',fooling_rate)\n", 62 | " for param in model.parameters():\n", 63 | " # 冻结梯度更新\n", 64 | " param.requires_grad = False\n", 65 | " return fooling_rate,model\n", 66 | "def universal_adversarial_perturbation(data_list,model,device,xi=10,\n", 67 | " delta=0.2,max_iter_uni=10,p=np.inf,\n", 68 | " num_classes=10,overshoot=0.02,\n", 69 | " max_iter_df=10,t_p=0.2):\n", 70 | " '''\n", 71 | " data_list: 图像路经\n", 72 | " model：目标模型\n", 73 | " device：计算设备\n", 74 | " xi:控制着p阶范式的扰动程度\n", 75 | " delta: 控制愚弄率，默认80%\n", 76 | " max_iter_uni: 最大迭代次数\n", 77 | " p: 正则化阶数\n", 78 | " num_classes 用于deepfool，限制测试的类别，默认为10\n", 79 | " overshoot：用于deepfool，防止无效更新\n", 80 | " max_iter_df:最大的deepfool的迭代次数\n", 81 | " t_p:用于deepfool，真实的比例，默认为0.2\n", 82 | " 函数返回通用扰动矩阵\n", 83 | " '''\n", 84 | " time_start=time.time()\n", 85 | " mean,std,tf=data_input_init(xi)\n", 86 | " v=torch.zeros(1,3,224,224).to(device)\n", 87 | " v.requires_grad_()\n", 88 | " fooling_rate=0.0\n", 89 | " num_images = len(data_list)\n", 90 | " itr = 0\n", 91 | " while fooling_rate<1-delta and itr 1.0:\n", 33 | " original_img=original_img/255.0\n", 34 | " if adversarial_img.any() > 1.0:\n", 35 | " adversarial_img=adversarial_img/255.0\n", 36 | "\n", 37 | " plt.subplot(131)\n", 38 | " plt.title('Original')\n", 39 | " plt.imshow(original_img)\n", 40 | " plt.axis('off')\n", 41 | "\n", 42 | " plt.subplot(132)\n", 43 | " plt.title('Adversarial')\n", 44 | " plt.imshow(adversarial_img)\n", 45 | " plt.axis('off')\n", 46 | "\n", 47 | " plt.subplot(133)\n", 48 | " plt.title('Adversarial-Original')\n", 49 | " difference = adversarial_img - original_img\n", 50 | " #(-1,1) -> (0,1)\n", 51 | " difference=difference / abs(difference).max()/2.0+0.5\n", 52 | " plt.imshow(difference,cmap=plt.cm.gray)\n", 53 | " plt.axis('off')\n", 54 | " plt.tight_layout()\n", 55 | " plt.show()\n", 56 | "# 获取计算设备\n", 57 | "device=torch.device('cuda'if torch.cuda.is_available() else 'cpu')\n", 58 | "#图像加载以及预处理\n", 59 | "image_path=\"../picture/horse.jpeg\"\n", 60 | "orig = cv2.imread(image_path)[..., ::-1]\n", 61 | "orig = cv2.resize(orig, (224, 224))\n", 62 | "img=orig.copy().astype(np.float32)\n", 63 | "mean = [0.485, 0.456, 0.406]\n", 64 | "std = [0.229, 0.224, 0.225]\n", 65 | "img /= 255.0\n", 66 | "img = (img - mean) / std\n", 67 | "img=img.transpose(2,0,1)\n", 68 | "img=np.expand_dims(img,axis=0)\n", 69 | "img=Variable(torch.from_numpy(img).to(device).float())\n", 70 | "model=models.alexnet(pretrained=True).to(device).eval()\n", 71 | "orig_label=np.argmax(model(img).data.cpu().numpy())\n", 72 | "print('orig_label={}'.format(orig_label))" 73 | ] 74 | }, 75 | { 76 | "cell_type": "code", 77 | "execution_count": 4, 78 | "metadata": { 79 | "collapsed": true 80 | }, 81 | "outputs": [], 82 | "source": [ 83 | "# Saliency map\n", 84 | "# 此方法为beta参数的简化版本，注重攻击目标贡献大的点\n", 85 | "def saliency_map(F,x,t,mask):\n", 86 | " # F 为模型的输出\n", 87 | " # t 为攻击的类别\n", 88 | " # x 表示输入的图像\n", 89 | " # mask 标记位，记录已经访问的点的坐标\n", 90 | " F[0,t].backward(retain_graph=True)\n", 91 | " derivative=x.grad.data.cpu().numpy().copy()\n", 92 | " alphas=derivative*mask # 预测 对攻击目标的贡献\n", 93 | " betas=-np.ones_like(alphas) # 预测对非攻击目标的贡献\n", 94 | " sal_map=np.abs(alphas)*np.abs(betas)*np.sign(alphas*betas)\n", 95 | " idx=np.argmin(sal_map) # 最佳像素和扰动方向\n", 96 | " idx=np.unravel_index(idx,mask.shape) # 转换成(p1,p2)格式\n", 97 | " pix_sign=np.sign(alphas)[idx]\n", 98 | " return idx,pix_sign" 99 | ] 100 | }, 101 | { 102 | "cell_type": "code", 103 | "execution_count": 8, 104 | "metadata": {}, 105 | "outputs": [ 106 | { 107 | "name": "stdout", 108 | "output_type": "stream", 109 | "text": [ 110 | "epoch=0 label=708 loss=10.877062797546387\n", 111 | "idx=(0, 1, 68, 111) over 6.442296981811523\n", 112 | "epoch=1 label=708 loss=10.73783016204834\n", 113 | "idx=(0, 1, 91, 81) over 6.550559997558594\n", 114 | "epoch=2 label=708 loss=10.609870910644531\n", 115 | "epoch=3 label=708 loss=10.493537902832031\n", 116 | "epoch=4 label=708 loss=10.32066535949707\n", 117 | "idx=(0, 1, 119, 92) over 4.274649620056152\n", 118 | "epoch=5 label=708 loss=10.159882545471191\n", 119 | "epoch=6 label=708 loss=10.056227684020996\n", 120 | "epoch=7 label=708 loss=9.946487426757812\n", 121 | "epoch=8 label=708 loss=9.85370922088623\n", 122 | "epoch=9 label=708 loss=9.754996299743652\n", 123 | "epoch=10 label=708 loss=9.689464569091797\n", 124 | "epoch=11 label=708 loss=9.55824089050293\n", 125 | "epoch=12 label=708 loss=9.371283531188965\n", 126 | "epoch=13 label=708 loss=9.216536521911621\n", 127 | "epoch=14 label=708 loss=9.074386596679688\n", 128 | "idx=(0, 1, 101, 162) over 3.1574230194091797\n", 129 | "epoch=15 label=708 loss=8.930526733398438\n", 130 | "epoch=16 label=708 loss=8.847480773925781\n", 131 | "epoch=17 label=708 loss=8.76241683959961\n", 132 | "epoch=18 label=708 loss=8.656790733337402\n", 133 | "epoch=19 label=708 loss=8.589019775390625\n", 134 | "idx=(0, 1, 107, 107) over 4.204621315002441\n", 135 | "epoch=20 label=51 loss=8.585227966308594\n", 136 | "epoch=21 label=51 loss=8.536836624145508\n", 137 | "epoch=22 label=51 loss=8.461435317993164\n", 138 | "epoch=23 label=51 loss=8.410560607910156\n", 139 | "idx=(0, 1, 109, 145) over 4.379692077636719\n", 140 | "epoch=24 label=364 loss=8.418410301208496\n", 141 | "idx=(0, 0, 101, 162) over 3.0404486656188965\n", 142 | "epoch=25 label=364 loss=8.325776100158691\n", 143 | "epoch=26 label=364 loss=8.296793937683105\n", 144 | "epoch=27 label=364 loss=8.281131744384766\n", 145 | "epoch=28 label=364 loss=8.216582298278809\n", 146 | "idx=(0, 0, 98, 136) over 4.53220272064209\n", 147 | "epoch=29 label=364 loss=8.19058609008789\n", 148 | "epoch=30 label=364 loss=8.196134567260742\n", 149 | "epoch=31 label=364 loss=8.22438907623291\n", 150 | "epoch=32 label=364 loss=8.171110153198242\n", 151 | "epoch=33 label=364 loss=8.069096565246582\n", 152 | "epoch=34 label=51 loss=7.974162578582764\n", 153 | "epoch=35 label=51 loss=7.879168510437012\n", 154 | "epoch=36 label=51 loss=7.757537841796875\n", 155 | "epoch=37 label=51 loss=7.719779014587402\n", 156 | "epoch=38 label=51 loss=7.712818145751953\n", 157 | "epoch=39 label=51 loss=7.605422019958496\n", 158 | "epoch=40 label=51 loss=7.512378692626953\n", 159 | "epoch=41 label=51 loss=7.458972930908203\n", 160 | "epoch=42 label=51 loss=7.52679967880249\n", 161 | "epoch=43 label=51 loss=7.419553279876709\n", 162 | "epoch=44 label=51 loss=7.331740856170654\n", 163 | "epoch=45 label=51 loss=7.3014044761657715\n", 164 | "epoch=46 label=51 loss=7.335504055023193\n", 165 | "epoch=47 label=51 loss=7.22821044921875\n", 166 | "epoch=48 label=51 loss=7.142072677612305\n", 167 | "epoch=49 label=51 loss=7.287581920623779\n", 168 | "epoch=50 label=51 loss=7.129010200500488\n", 169 | "idx=(0, 0, 100, 138) over 4.258206844329834\n", 170 | "epoch=51 label=51 loss=7.112462997436523\n", 171 | "idx=(0, 1, 100, 138) over 4.362184524536133\n", 172 | "epoch=52 label=51 loss=7.227566242218018\n", 173 | "epoch=53 label=51 loss=7.08453893661499\n", 174 | "epoch=54 label=51 loss=6.921497821807861\n", 175 | "epoch=55 label=51 loss=6.790563583374023\n", 176 | "epoch=56 label=51 loss=6.680903434753418\n", 177 | "epoch=57 label=51 loss=6.625180721282959\n", 178 | "idx=(0, 0, 109, 145) over 4.275331497192383\n", 179 | "epoch=58 label=51 loss=6.436946392059326\n", 180 | "epoch=59 label=51 loss=6.4111104011535645\n", 181 | "epoch=60 label=51 loss=6.3189005851745605\n", 182 | "epoch=61 label=51 loss=6.194986343383789\n", 183 | "epoch=62 label=51 loss=6.107163429260254\n", 184 | "epoch=63 label=51 loss=6.045999050140381\n", 185 | "epoch=64 label=51 loss=5.987514019012451\n", 186 | "epoch=65 label=51 loss=5.891438961029053\n", 187 | "epoch=66 label=51 loss=5.690794944763184\n", 188 | "epoch=67 label=51 loss=5.518342018127441\n", 189 | "epoch=68 label=51 loss=5.409573554992676\n", 190 | "epoch=69 label=51 loss=5.302094459533691\n", 191 | "epoch=70 label=51 loss=5.208126068115234\n", 192 | "epoch=71 label=51 loss=5.143712997436523\n", 193 | "epoch=72 label=51 loss=5.0477423667907715\n", 194 | "idx=(0, 0, 122, 152) over 4.395205020904541\n", 195 | "epoch=73 label=51 loss=4.998594760894775\n", 196 | "epoch=74 label=51 loss=4.874507904052734\n", 197 | "epoch=75 label=51 loss=4.7626872062683105\n", 198 | "idx=(0, 1, 100, 158) over 3.9802520275115967\n", 199 | "epoch=76 label=51 loss=4.684852123260498\n", 200 | "idx=(0, 0, 100, 158) over 3.8453121185302734\n", 201 | "epoch=77 label=51 loss=4.514315128326416\n", 202 | "epoch=78 label=51 loss=4.472990036010742\n", 203 | "epoch=79 label=51 loss=4.331677436828613\n", 204 | "epoch=80 label=51 loss=4.260552406311035\n", 205 | "epoch=81 label=51 loss=4.0974578857421875\n", 206 | "epoch=82 label=51 loss=4.079051971435547\n", 207 | "epoch=83 label=51 loss=4.008029937744141\n", 208 | "epoch=84 label=51 loss=3.9733781814575195\n", 209 | "epoch=85 label=51 loss=3.9497289657592773\n", 210 | "epoch=86 label=51 loss=3.902484893798828\n", 211 | "epoch=87 label=51 loss=3.817145347595215\n", 212 | "epoch=88 label=51 loss=3.7644271850585938\n", 213 | "epoch=89 label=51 loss=3.697509765625\n", 214 | "epoch=90 label=51 loss=3.604391098022461\n", 215 | "epoch=91 label=51 loss=3.4687490463256836\n", 216 | "epoch=92 label=51 loss=3.427938461303711\n", 217 | "epoch=93 label=51 loss=3.364107131958008\n", 218 | "epoch=94 label=51 loss=3.2636938095092773\n", 219 | "epoch=95 label=51 loss=3.1871204376220703\n", 220 | "epoch=96 label=51 loss=3.1775598526000977\n", 221 | "epoch=97 label=51 loss=3.3098020553588867\n", 222 | "epoch=98 label=51 loss=3.2290725708007812\n", 223 | "epoch=99 label=51 loss=3.158574104309082\n", 224 | "epoch=100 label=51 loss=3.05307674407959\n", 225 | "epoch=101 label=51 loss=2.9604082107543945\n", 226 | "epoch=102 label=51 loss=2.8453025817871094\n", 227 | "epoch=103 label=51 loss=2.7262487411499023\n", 228 | "idx=(0, 0, 103, 136) over 4.480828762054443\n", 229 | "epoch=104 label=51 loss=2.64224910736084\n", 230 | "idx=(0, 0, 104, 122) over 3.87956166267395\n", 231 | "epoch=105 label=51 loss=2.536820411682129\n", 232 | "epoch=106 label=51 loss=2.4196996688842773\n", 233 | "epoch=107 label=51 loss=2.30173397064209\n", 234 | "epoch=108 label=51 loss=2.3065052032470703\n", 235 | "epoch=109 label=51 loss=2.251315116882324\n", 236 | "idx=(0, 1, 100, 144) over 4.327170372009277\n", 237 | "epoch=110 label=51 loss=2.175297737121582\n", 238 | "epoch=111 label=51 loss=2.0631160736083984\n", 239 | "idx=(0, 0, 100, 144) over 4.223957061767578\n", 240 | "epoch=112 label=51 loss=1.9604244232177734\n", 241 | "epoch=113 label=51 loss=1.9469337463378906\n", 242 | "epoch=114 label=51 loss=1.8308420181274414\n", 243 | "epoch=115 label=51 loss=1.7953052520751953\n", 244 | "epoch=116 label=51 loss=1.6912193298339844\n", 245 | "epoch=117 label=51 loss=1.6115522384643555\n", 246 | "idx=(0, 0, 105, 122) over 3.5713160037994385\n", 247 | "epoch=118 label=51 loss=1.5583915710449219\n", 248 | "epoch=119 label=51 loss=1.5074138641357422\n", 249 | "idx=(0, 1, 104, 122) over 4.015266418457031\n", 250 | "epoch=120 label=51 loss=1.4632511138916016\n", 251 | "epoch=121 label=51 loss=1.3992414474487305\n", 252 | "idx=(0, 1, 91, 124) over 3.06988787651062\n", 253 | "epoch=122 label=288 loss=1.3142623901367188\n" 254 | ] 255 | } 256 | ], 257 | "source": [ 258 | "img.requires_grad=True\n", 259 | "for param in model.parameters():\n", 260 | " param.requires_grad=False\n", 261 | "epochs=500\n", 262 | "theta=0.3 # 扰动系数\n", 263 | "target_label=288 #攻击目标\n", 264 | "target=Variable(torch.Tensor([float(target_label)]).to(device).long())\n", 265 | "loss_func=torch.nn.CrossEntropyLoss()\n", 266 | "\n", 267 | "mask=np.ones_like(img.data.cpu().numpy()) # 定义搜索域，修改后的位置置零\n", 268 | "# 定义边界\n", 269 | "max_=3.0\n", 270 | "min_=-3.0\n", 271 | "for epoch in range(epochs):\n", 272 | " output=model(img)\n", 273 | " label=np.argmax(output.data.cpu().numpy())\n", 274 | " loss=loss_func(output,target)\n", 275 | " print('epoch={} label={} loss={}'.format(epoch,label,loss))\n", 276 | " if label==target_label:\n", 277 | " break # 攻击成功\n", 278 | " zero_gradients(img) # 梯度清零\n", 279 | " idx,pix_sign=saliency_map(output,img,target_label,mask)\n", 280 | " # 添加扰动\n", 281 | " img.data[idx]=img.data[idx]+pix_sign*theta*(max_ - min_)\n", 282 | " # 达到极限的点不再参与更新\n", 283 | " if(img.data[idx] <= min_)or(img.data[idx] >= max_):\n", 284 | " print('idx={} over {}'.format(idx,img.data[idx]))\n", 285 | " mask[idx]=0\n", 286 | " img.data.cpu()[idx]=np.clip(img.data.cpu()[idx],min_,max_)" 287 | ] 288 | }, 289 | { 290 | "cell_type": "code", 291 | "execution_count": 11, 292 | "metadata": {}, 293 | "outputs": [ 294 | { 295 | "name": "stdout", 296 | "output_type": "stream", 297 | "text": [ 298 | "(3, 224, 224)\n" 299 | ] 300 | }, 301 | { 302 | "data": { 303 | "image/png": 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\n", 304 | "text/plain": [ 305 | "
" 306 | ] 307 | }, 308 | "metadata": {}, 309 | "output_type": "display_data" 310 | } 311 | ], 312 | "source": [ 313 | "adv=img.data.cpu().numpy()[0]\n", 314 | "print(adv.shape)\n", 315 | "adv = adv.transpose(1, 2, 0)\n", 316 | "adv = (adv * std) + mean\n", 317 | "\n", 318 | "adv = adv * 256.0\n", 319 | "adv = np.clip(adv, 0, 255).astype(np.uint8)\n", 320 | "\n", 321 | "show_images_diff(orig,orig_label,adv,target_label)" 322 | ] 323 | }, 324 | { 325 | "cell_type": "code", 326 | "execution_count": null, 327 | "metadata": { 328 | "collapsed": true 329 | }, 330 | "outputs": [], 331 | "source": [] 332 | } 333 | ], 334 | "metadata": { 335 | "kernelspec": { 336 | "display_name": "Python 3", 337 | "language": "python", 338 | "name": "python3" 339 | }, 340 | "language_info": { 341 | "codemirror_mode": { 342 | "name": "ipython", 343 | "version": 3 344 | }, 345 | "file_extension": ".py", 346 | "mimetype": "text/x-python", 347 | "name": "python", 348 | "nbconvert_exporter": "python", 349 | "pygments_lexer": "ipython3", 350 | "version": "3.6.8" 351 | } 352 | }, 353 | "nbformat": 4, 354 | "nbformat_minor": 2 355 | } 356 | -------------------------------------------------------------------------------- /黑盒/JSMA/JSMA.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 3, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stdout", 10 | "output_type": "stream", 11 | "text": [ 12 | "orig_label=708\n" 13 | ] 14 | } 15 | ], 16 | "source": [ 17 | "# 基于l0范式，JSMA\n", 18 | "import torch\n", 19 | "import torchvision\n", 20 | "from torch.autograd import Variable\n", 21 | "from torch.autograd.gradcheck import zero_gradients\n", 22 | "import torch.nn as nn\n", 23 | "from torchvision import models\n", 24 | "import numpy as np\n", 25 | "import cv2\n", 26 | "#对比展现原始图片和对抗样本图片\n", 27 | "def show_images_diff(original_img,original_label,adversarial_img,adversarial_label):\n", 28 | " import matplotlib.pyplot as plt\n", 29 | " plt.figure()\n", 30 | "\n", 31 | " #归一化\n", 32 | " if original_img.any() > 1.0:\n", 33 | " original_img=original_img/255.0\n", 34 | " if adversarial_img.any() > 1.0:\n", 35 | " adversarial_img=adversarial_img/255.0\n", 36 | "\n", 37 | " plt.subplot(131)\n", 38 | " plt.title('Original')\n", 39 | " plt.imshow(original_img)\n", 40 | " plt.axis('off')\n", 41 | "\n", 42 | " plt.subplot(132)\n", 43 | " plt.title('Adversarial')\n", 44 | " plt.imshow(adversarial_img)\n", 45 | " plt.axis('off')\n", 46 | "\n", 47 | " plt.subplot(133)\n", 48 | " plt.title('Adversarial-Original')\n", 49 | " difference = adversarial_img - original_img\n", 50 | " #(-1,1) -> (0,1)\n", 51 | " difference=difference / abs(difference).max()/2.0+0.5\n", 52 | " plt.imshow(difference,cmap=plt.cm.gray)\n", 53 | " plt.axis('off')\n", 54 | " plt.tight_layout()\n", 55 | " plt.show()\n", 56 | "# 获取计算设备\n", 57 | "device=torch.device('cuda'if torch.cuda.is_available() else 'cpu')\n", 58 | "#图像加载以及预处理\n", 59 | "image_path=\"../picture/horse.jpeg\"\n", 60 | "orig = cv2.imread(image_path)[..., ::-1]\n", 61 | "orig = cv2.resize(orig, (224, 224))\n", 62 | "img=orig.copy().astype(np.float32)\n", 63 | "mean = [0.485, 0.456, 0.406]\n", 64 | "std = [0.229, 0.224, 0.225]\n", 65 | "img /= 255.0\n", 66 | "img = (img - mean) / std\n", 67 | "img=img.transpose(2,0,1)\n", 68 | "img=np.expand_dims(img,axis=0)\n", 69 | "img=Variable(torch.from_numpy(img).to(device).float())\n", 70 | "model=models.alexnet(pretrained=True).to(device).eval()\n", 71 | "orig_label=np.argmax(model(img).data.cpu().numpy())\n", 72 | "print('orig_label={}'.format(orig_label))" 73 | ] 74 | }, 75 | { 76 | "cell_type": "code", 77 | "execution_count": 4, 78 | "metadata": { 79 | "collapsed": true 80 | }, 81 | "outputs": [], 82 | "source": [ 83 | "# Saliency map\n", 84 | "# 此方法为beta参数的简化版本，注重攻击目标贡献大的点\n", 85 | "def saliency_map(F,x,t,mask):\n", 86 | " # F 为模型的输出\n", 87 | " # t 为攻击的类别\n", 88 | " # x 表示输入的图像\n", 89 | " # mask 标记位，记录已经访问的点的坐标\n", 90 | " F[0,t].backward(retain_graph=True)\n", 91 | " derivative=x.grad.data.cpu().numpy().copy()\n", 92 | " alphas=derivative*mask # 预测 对攻击目标的贡献\n", 93 | " betas=-np.ones_like(alphas) # 预测对非攻击目标的贡献\n", 94 | " sal_map=np.abs(alphas)*np.abs(betas)*np.sign(alphas*betas)\n", 95 | " idx=np.argmin(sal_map) # 最佳像素和扰动方向\n", 96 | " idx=np.unravel_index(idx,mask.shape) # 转换成(p1,p2)格式\n", 97 | " pix_sign=np.sign(alphas)[idx]\n", 98 | " return idx,pix_sign" 99 | ] 100 | }, 101 | { 102 | "cell_type": "code", 103 | "execution_count": 8, 104 | "metadata": {}, 105 | "outputs": [ 106 | { 107 | "name": "stdout", 108 | "output_type": "stream", 109 | "text": [ 110 | "epoch=0 label=708 loss=10.877062797546387\n", 111 | "idx=(0, 1, 68, 111) over 6.442296981811523\n", 112 | "epoch=1 label=708 loss=10.73783016204834\n", 113 | "idx=(0, 1, 91, 81) over 6.550559997558594\n", 114 | "epoch=2 label=708 loss=10.609870910644531\n", 115 | "epoch=3 label=708 loss=10.493537902832031\n", 116 | "epoch=4 label=708 loss=10.32066535949707\n", 117 | "idx=(0, 1, 119, 92) over 4.274649620056152\n", 118 | "epoch=5 label=708 loss=10.159882545471191\n", 119 | "epoch=6 label=708 loss=10.056227684020996\n", 120 | "epoch=7 label=708 loss=9.946487426757812\n", 121 | "epoch=8 label=708 loss=9.85370922088623\n", 122 | "epoch=9 label=708 loss=9.754996299743652\n", 123 | "epoch=10 label=708 loss=9.689464569091797\n", 124 | "epoch=11 label=708 loss=9.55824089050293\n", 125 | "epoch=12 label=708 loss=9.371283531188965\n", 126 | "epoch=13 label=708 loss=9.216536521911621\n", 127 | "epoch=14 label=708 loss=9.074386596679688\n", 128 | "idx=(0, 1, 101, 162) over 3.1574230194091797\n", 129 | "epoch=15 label=708 loss=8.930526733398438\n", 130 | "epoch=16 label=708 loss=8.847480773925781\n", 131 | "epoch=17 label=708 loss=8.76241683959961\n", 132 | "epoch=18 label=708 loss=8.656790733337402\n", 133 | "epoch=19 label=708 loss=8.589019775390625\n", 134 | "idx=(0, 1, 107, 107) over 4.204621315002441\n", 135 | "epoch=20 label=51 loss=8.585227966308594\n", 136 | "epoch=21 label=51 loss=8.536836624145508\n", 137 | "epoch=22 label=51 loss=8.461435317993164\n", 138 | "epoch=23 label=51 loss=8.410560607910156\n", 139 | "idx=(0, 1, 109, 145) over 4.379692077636719\n", 140 | "epoch=24 label=364 loss=8.418410301208496\n", 141 | "idx=(0, 0, 101, 162) over 3.0404486656188965\n", 142 | "epoch=25 label=364 loss=8.325776100158691\n", 143 | "epoch=26 label=364 loss=8.296793937683105\n", 144 | "epoch=27 label=364 loss=8.281131744384766\n", 145 | "epoch=28 label=364 loss=8.216582298278809\n", 146 | "idx=(0, 0, 98, 136) over 4.53220272064209\n", 147 | "epoch=29 label=364 loss=8.19058609008789\n", 148 | "epoch=30 label=364 loss=8.196134567260742\n", 149 | "epoch=31 label=364 loss=8.22438907623291\n", 150 | "epoch=32 label=364 loss=8.171110153198242\n", 151 | "epoch=33 label=364 loss=8.069096565246582\n", 152 | "epoch=34 label=51 loss=7.974162578582764\n", 153 | "epoch=35 label=51 loss=7.879168510437012\n", 154 | "epoch=36 label=51 loss=7.757537841796875\n", 155 | "epoch=37 label=51 loss=7.719779014587402\n", 156 | "epoch=38 label=51 loss=7.712818145751953\n", 157 | "epoch=39 label=51 loss=7.605422019958496\n", 158 | "epoch=40 label=51 loss=7.512378692626953\n", 159 | "epoch=41 label=51 loss=7.458972930908203\n", 160 | "epoch=42 label=51 loss=7.52679967880249\n", 161 | "epoch=43 label=51 loss=7.419553279876709\n", 162 | "epoch=44 label=51 loss=7.331740856170654\n", 163 | "epoch=45 label=51 loss=7.3014044761657715\n", 164 | "epoch=46 label=51 loss=7.335504055023193\n", 165 | "epoch=47 label=51 loss=7.22821044921875\n", 166 | "epoch=48 label=51 loss=7.142072677612305\n", 167 | "epoch=49 label=51 loss=7.287581920623779\n", 168 | "epoch=50 label=51 loss=7.129010200500488\n", 169 | "idx=(0, 0, 100, 138) over 4.258206844329834\n", 170 | "epoch=51 label=51 loss=7.112462997436523\n", 171 | "idx=(0, 1, 100, 138) over 4.362184524536133\n", 172 | "epoch=52 label=51 loss=7.227566242218018\n", 173 | "epoch=53 label=51 loss=7.08453893661499\n", 174 | "epoch=54 label=51 loss=6.921497821807861\n", 175 | "epoch=55 label=51 loss=6.790563583374023\n", 176 | "epoch=56 label=51 loss=6.680903434753418\n", 177 | "epoch=57 label=51 loss=6.625180721282959\n", 178 | "idx=(0, 0, 109, 145) over 4.275331497192383\n", 179 | "epoch=58 label=51 loss=6.436946392059326\n", 180 | "epoch=59 label=51 loss=6.4111104011535645\n", 181 | "epoch=60 label=51 loss=6.3189005851745605\n", 182 | "epoch=61 label=51 loss=6.194986343383789\n", 183 | "epoch=62 label=51 loss=6.107163429260254\n", 184 | "epoch=63 label=51 loss=6.045999050140381\n", 185 | "epoch=64 label=51 loss=5.987514019012451\n", 186 | "epoch=65 label=51 loss=5.891438961029053\n", 187 | "epoch=66 label=51 loss=5.690794944763184\n", 188 | "epoch=67 label=51 loss=5.518342018127441\n", 189 | "epoch=68 label=51 loss=5.409573554992676\n", 190 | "epoch=69 label=51 loss=5.302094459533691\n", 191 | "epoch=70 label=51 loss=5.208126068115234\n", 192 | "epoch=71 label=51 loss=5.143712997436523\n", 193 | "epoch=72 label=51 loss=5.0477423667907715\n", 194 | "idx=(0, 0, 122, 152) over 4.395205020904541\n", 195 | "epoch=73 label=51 loss=4.998594760894775\n", 196 | "epoch=74 label=51 loss=4.874507904052734\n", 197 | "epoch=75 label=51 loss=4.7626872062683105\n", 198 | "idx=(0, 1, 100, 158) over 3.9802520275115967\n", 199 | "epoch=76 label=51 loss=4.684852123260498\n", 200 | "idx=(0, 0, 100, 158) over 3.8453121185302734\n", 201 | "epoch=77 label=51 loss=4.514315128326416\n", 202 | "epoch=78 label=51 loss=4.472990036010742\n", 203 | "epoch=79 label=51 loss=4.331677436828613\n", 204 | "epoch=80 label=51 loss=4.260552406311035\n", 205 | "epoch=81 label=51 loss=4.0974578857421875\n", 206 | "epoch=82 label=51 loss=4.079051971435547\n", 207 | "epoch=83 label=51 loss=4.008029937744141\n", 208 | "epoch=84 label=51 loss=3.9733781814575195\n", 209 | "epoch=85 label=51 loss=3.9497289657592773\n", 210 | "epoch=86 label=51 loss=3.902484893798828\n", 211 | "epoch=87 label=51 loss=3.817145347595215\n", 212 | "epoch=88 label=51 loss=3.7644271850585938\n", 213 | "epoch=89 label=51 loss=3.697509765625\n", 214 | "epoch=90 label=51 loss=3.604391098022461\n", 215 | "epoch=91 label=51 loss=3.4687490463256836\n", 216 | "epoch=92 label=51 loss=3.427938461303711\n", 217 | "epoch=93 label=51 loss=3.364107131958008\n", 218 | "epoch=94 label=51 loss=3.2636938095092773\n", 219 | "epoch=95 label=51 loss=3.1871204376220703\n", 220 | "epoch=96 label=51 loss=3.1775598526000977\n", 221 | "epoch=97 label=51 loss=3.3098020553588867\n", 222 | "epoch=98 label=51 loss=3.2290725708007812\n", 223 | "epoch=99 label=51 loss=3.158574104309082\n", 224 | "epoch=100 label=51 loss=3.05307674407959\n", 225 | "epoch=101 label=51 loss=2.9604082107543945\n", 226 | "epoch=102 label=51 loss=2.8453025817871094\n", 227 | "epoch=103 label=51 loss=2.7262487411499023\n", 228 | "idx=(0, 0, 103, 136) over 4.480828762054443\n", 229 | "epoch=104 label=51 loss=2.64224910736084\n", 230 | "idx=(0, 0, 104, 122) over 3.87956166267395\n", 231 | "epoch=105 label=51 loss=2.536820411682129\n", 232 | "epoch=106 label=51 loss=2.4196996688842773\n", 233 | "epoch=107 label=51 loss=2.30173397064209\n", 234 | "epoch=108 label=51 loss=2.3065052032470703\n", 235 | "epoch=109 label=51 loss=2.251315116882324\n", 236 | "idx=(0, 1, 100, 144) over 4.327170372009277\n", 237 | "epoch=110 label=51 loss=2.175297737121582\n", 238 | "epoch=111 label=51 loss=2.0631160736083984\n", 239 | "idx=(0, 0, 100, 144) over 4.223957061767578\n", 240 | "epoch=112 label=51 loss=1.9604244232177734\n", 241 | "epoch=113 label=51 loss=1.9469337463378906\n", 242 | "epoch=114 label=51 loss=1.8308420181274414\n", 243 | "epoch=115 label=51 loss=1.7953052520751953\n", 244 | "epoch=116 label=51 loss=1.6912193298339844\n", 245 | "epoch=117 label=51 loss=1.6115522384643555\n", 246 | "idx=(0, 0, 105, 122) over 3.5713160037994385\n", 247 | "epoch=118 label=51 loss=1.5583915710449219\n", 248 | "epoch=119 label=51 loss=1.5074138641357422\n", 249 | "idx=(0, 1, 104, 122) over 4.015266418457031\n", 250 | "epoch=120 label=51 loss=1.4632511138916016\n", 251 | "epoch=121 label=51 loss=1.3992414474487305\n", 252 | "idx=(0, 1, 91, 124) over 3.06988787651062\n", 253 | "epoch=122 label=288 loss=1.3142623901367188\n" 254 | ] 255 | } 256 | ], 257 | "source": [ 258 | "img.requires_grad=True\n", 259 | "for param in model.parameters():\n", 260 | " param.requires_grad=False\n", 261 | "epochs=500\n", 262 | "theta=0.3 # 扰动系数\n", 263 | "target_label=288 #攻击目标\n", 264 | "target=Variable(torch.Tensor([float(target_label)]).to(device).long())\n", 265 | "loss_func=torch.nn.CrossEntropyLoss()\n", 266 | "\n", 267 | "mask=np.ones_like(img.data.cpu().numpy()) # 定义搜索域，修改后的位置置零\n", 268 | "# 定义边界\n", 269 | "max_=3.0\n", 270 | "min_=-3.0\n", 271 | "for epoch in range(epochs):\n", 272 | " output=model(img)\n", 273 | " label=np.argmax(output.data.cpu().numpy())\n", 274 | " loss=loss_func(output,target)\n", 275 | " print('epoch={} label={} loss={}'.format(epoch,label,loss))\n", 276 | " if label==target_label:\n", 277 | " break # 攻击成功\n", 278 | " zero_gradients(img) # 梯度清零\n", 279 | " idx,pix_sign=saliency_map(output,img,target_label,mask)\n", 280 | " # 添加扰动\n", 281 | " img.data[idx]=img.data[idx]+pix_sign*theta*(max_ - min_)\n", 282 | " # 达到极限的点不再参与更新\n", 283 | " if(img.data[idx] <= min_)or(img.data[idx] >= max_):\n", 284 | " print('idx={} over {}'.format(idx,img.data[idx]))\n", 285 | " mask[idx]=0\n", 286 | " img.data.cpu()[idx]=np.clip(img.data.cpu()[idx],min_,max_)" 287 | ] 288 | }, 289 | { 290 | "cell_type": "code", 291 | "execution_count": 11, 292 | "metadata": {}, 293 | "outputs": [ 294 | { 295 | "name": "stdout", 296 | "output_type": "stream", 297 | "text": [ 298 | "(3, 224, 224)\n" 299 | ] 300 | }, 301 | { 302 | "data": { 303 | "image/png": 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\n", 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" 306 | ] 307 | }, 308 | "metadata": {}, 309 | "output_type": "display_data" 310 | } 311 | ], 312 | "source": [ 313 | "adv=img.data.cpu().numpy()[0]\n", 314 | "print(adv.shape)\n", 315 | "adv = adv.transpose(1, 2, 0)\n", 316 | "adv = (adv * std) + mean\n", 317 | "\n", 318 | "adv = adv * 256.0\n", 319 | "adv = np.clip(adv, 0, 255).astype(np.uint8)\n", 320 | "\n", 321 | "show_images_diff(orig,orig_label,adv,target_label)" 322 | ] 323 | }, 324 | { 325 | "cell_type": "code", 326 | "execution_count": null, 327 | "metadata": { 328 | "collapsed": true 329 | }, 330 | "outputs": [], 331 | "source": [] 332 | } 333 | ], 334 | "metadata": { 335 | "kernelspec": { 336 | "display_name": "Python 3", 337 | "language": "python", 338 | "name": "python3" 339 | }, 340 | "language_info": { 341 | "codemirror_mode": { 342 | "name": "ipython", 343 | "version": 3 344 | }, 345 | "file_extension": ".py", 346 | "mimetype": "text/x-python", 347 | "name": "python", 348 | "nbconvert_exporter": "python", 349 | "pygments_lexer": "ipython3", 350 | "version": "3.6.8" 351 | } 352 | }, 353 | "nbformat": 4, 354 | "nbformat_minor": 2 355 | } 356 | --------------------------------------------------------------------------------