foolbox.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import keras\n",
    "from keras.backend.tensorflow_backend import set_session\n",
    "import os\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\"  \n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"1\"\n",
    "config = tf.ConfigProto()\n",
    "config.gpu_options.per_process_gpu_memory_fraction = 0.7\n",
    "\n",
    "keras.backend.set_learning_phase(0)\n",
    "set_session(tf.Session(config=config))\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from keras.applications.inception_v3 import InceptionV3,preprocess_input\n",
    "import foolbox\n",
    "from keras.models import load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.4/dist-packages/keras/backend/tensorflow_backend.py:1349: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "keep_dims is deprecated, use keepdims instead\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.4/dist-packages/keras/backend/tensorflow_backend.py:2885: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "keep_dims is deprecated, use keepdims instead\n",
      "WARNING:tensorflow:Variable *= will be deprecated. Use variable.assign_mul if you want assignment to the variable value or 'x = x * y' if you want a new python Tensor object.\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing import image\n",
    "from foolbox.criteria import TargetClass\n",
    "\n",
    "model=load_model('/home/shikhar/notebooks/standard_imagenet_100_inceptiov3.h5') ## Inception v3\n",
    "fmodel=foolbox.models.KerasModel(model,bounds=(-1,1))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.0 1.0\n"
     ]
    }
   ],
   "source": [
    "img_path='/home/shikhar/Downloads/Imagenet_100/TEST/acoustic_guitar/guitar.jpg'\n",
    "img=image.load_img(img_path)\n",
    "img=img.resize((299,299))\n",
    "img=image.img_to_array(img)\n",
    "pre_img=preprocess_input (img)\n",
    "print (pre_img.min(),pre_img.max())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Running an attack that tries to minimize the Linfinity norm of the perturbation without specifying foolbox.distances.Linfinity as the distance metric might lead to suboptimal results.\n"
     ]
    }
   ],
   "source": [
    "label=np.argmax(model.predict(np.expand_dims(pre_img,axis=0)))\n",
    "print (label)\n",
    "attack=foolbox.attacks.RandomPGD(fmodel,TargetClass(11))\n",
    "adversarial=attack(pre_img,label,unpack=False)                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1=load_model('/home/shikhar/notebooks/semantic_imagenet_100_inceptionV3.h5')\n",
    "fmodel1=foolbox.models.KerasModel(model1,bounds=(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Running an attack that tries to minimize the Linfinity norm of the perturbation without specifying foolbox.distances.Linfinity as the distance metric might lead to suboptimal results.\n"
     ]
    }
   ],
   "source": [
    "label1=np.argmax(model1.predict(np.expand_dims(pre_img,axis=0)))\n",
    "print (label1)\n",
    "attack1=foolbox.attacks.RandomPGD(fmodel1,TargetClass(11))\n",
    "adversarial1=attack1(pre_img,label1,unpack=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "standard  1.78231362111043e-05\n",
      "semantic  6.142532629056862e-05\n"
     ]
    }
   ],
   "source": [
    "print ('standard ',adversarial.distance.value)\n",
    "print ('semantic ',adversarial1.distance.value)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "semantic- wallet | standard- wallet  || True ||   wallet\n",
      "semantic- wallet | standard- wallet  || True ||   wallet\n",
      "semantic- volcano | standard- volcano  || True ||   volcano\n",
      "semantic- bookshop | standard- library  || False ||   bookshop\n",
      "semantic- bookshop | standard- library  || False ||   bookshop\n",
      "semantic- crossword | standard- crossword  || True ||   crossword\n",
      "semantic- crossword | standard- crossword  || True ||   crossword\n",
      "semantic- sea_urchin | standard- sea_urchin  || True ||   sea_urchin\n",
      "semantic- restaurant | standard- restaurant  || True ||   restaurant\n",
      "semantic- assault_rifle | standard- restaurant  || True ||   restaurant\n",
      "semantic- pretzel | standard- pretzel  || True ||   pretzel\n",
      "semantic- pretzel | standard- pretzel  || True ||   pretzel\n",
      "semantic- hamster | standard- hamster  || True ||   hamster\n",
      "semantic- hamster | standard- polar_bear  || False ||   hamster\n",
      "semantic- television | standard- television  || True ||   television\n",
      "semantic- television | standard- television  || True ||   television\n",
      "semantic- castle | standard- castle  || True ||   castle\n",
      "semantic- candle | standard- candle  || True ||   candle\n",
      "semantic- candle | standard- candle  || True ||   candle\n",
      "semantic- staffordshire_bull_terrier | standard- staffordshire_bull_terrier  || True ||   staffordshire_bull_terrier\n",
      "semantic- staffordshire_bull_terrier | standard- staffordshire_bull_terrier  || True ||   staffordshire_bull_terrier\n",
      "semantic- polar_bear | standard- polar_bear  || True ||   polar_bear\n",
      "semantic- polar_bear | standard- polar_bear  || True ||   polar_bear\n",
      "semantic- ipod | standard- laptop  || True ||   laptop\n",
      "semantic- laptop | standard- laptop  || True ||   laptop\n",
      "semantic- speaker | standard- speaker  || False ||   radio\n",
      "semantic- radio | standard- radio  || True ||   radio\n",
      "semantic- peacock | standard- peacock  || True ||   peacock\n",
      "semantic- peacock | standard- peacock  || True ||   peacock\n",
      "semantic- ambulance | standard- ambulance  || True ||   ambulance\n",
      "semantic- firetruck | standard- ambulance  || True ||   ambulance\n",
      "semantic- billiard_table | standard- billiard_table  || True ||   billiard_table\n",
      "semantic- billiard_table | standard- printer  || False ||   billiard_table\n",
      "semantic- vase | standard- vase  || True ||   vase\n",
      "semantic- vase | standard- vase  || True ||   vase\n",
      "semantic- bathtub | standard- coffee_mug  || False ||   bathtub\n",
      "semantic- zebra | standard- zebra  || True ||   zebra\n",
      "semantic- zebra | standard- zebra  || True ||   zebra\n",
      "semantic- suit | standard- suit  || True ||   suit\n",
      "semantic- suit | standard- suit  || True ||   suit\n",
      "semantic- sunglass | standard- sunglass  || True ||   sunglass\n",
      "semantic- cellphone | standard- vase  || False ||   screw_driver\n",
      "semantic- golden_retriever | standard- golden_retriever  || True ||   golden_retriever\n",
      "semantic- golden_retriever | standard- golden_retriever  || True ||   golden_retriever\n",
      "semantic- cinema | standard- television  || False ||   cinema\n",
      "semantic- cinema | standard- cinema  || True ||   cinema\n",
      "semantic- dumbbell | standard- dumbbell  || True ||   dumbbell\n",
      "semantic- dumbbell | standard- speaker  || False ||   dumbbell\n",
      "semantic- cellphone | standard- cellphone  || True ||   cellphone\n",
      "semantic- ipod | standard- cellphone  || True ||   cellphone\n",
      "semantic- speaker | standard- speaker  || True ||   speaker\n",
      "semantic- speaker | standard- speaker  || True ||   speaker\n",
      "semantic- lion | standard- lion  || True ||   lion\n",
      "semantic- lion | standard- lion  || True ||   lion\n",
      "semantic- bakery | standard- bakery  || True ||   bakery\n",
      "semantic- dalmatian | standard- dalmatian  || True ||   dalmatian\n",
      "semantic- dalmatian | standard- dalmatian  || True ||   dalmatian\n",
      "semantic- aircraft_carrier | standard- aircraft_carrier  || True ||   aircraft_carrier\n",
      "semantic- panda | standard- panda  || True ||   panda\n",
      "semantic- panda | standard- panda  || True ||   panda\n",
      "semantic- paintbrush | standard- broom  || False ||   paintbrush\n",
      "semantic- radio | standard- lighter  || False ||   paintbrush\n",
      "semantic- piggybank | standard- piggybank  || True ||   piggybank\n",
      "semantic- umbrella | standard- piggybank  || True ||   piggybank\n",
      "semantic- printer | standard- printer  || False ||   umbrella\n",
      "semantic- langur | standard- langur  || True ||   langur\n",
      "semantic- tarantula | standard- tarantula  || True ||   tarantula\n",
      "semantic- tarantula | standard- tarantula  || True ||   tarantula\n",
      "semantic- dustbin | standard- bucket  || False ||   dustbin\n",
      "semantic- bucket | standard- bucket  || False ||   dustbin\n",
      "semantic- assault_rifle | standard- assault_rifle  || True ||   assault_rifle\n",
      "semantic- assault_rifle | standard- assault_rifle  || True ||   assault_rifle\n",
      "semantic- lighter | standard- perfume  || True ||   perfume\n",
      "semantic- perfume | standard- perfume  || True ||   perfume\n",
      "semantic- snail | standard- snail  || True ||   snail\n",
      "semantic- snail | standard- snail  || True ||   snail\n",
      "semantic- acoustic_guitar | standard- acoustic_guitar  || True ||   acoustic_guitar\n",
      "semantic- acoustic_guitar | standard- acoustic_guitar  || True ||   acoustic_guitar\n",
      "semantic- laptop | standard- laptop  || False ||   cab\n",
      "semantic- cab | standard- cab  || True ||   cab\n",
      "semantic- corn | standard- corn  || True ||   corn\n",
      "semantic- corn | standard- corn  || True ||   corn\n",
      "semantic- pomeranian | standard- pomeranian  || True ||   pomeranian\n"
     ]
    }
   ],
   "source": [
    "labels={'scuba_diver': 98, 'sea_urchin': 28, 'crossword': 93, 'chihuahua': 15, 'dumbbell': 57, 'printer': 76, 'mailbox': 65, 'cellphone': 53, 'balloon': 42, 'doberman': 18, 'mask': 66, 'bathtub': 44, 'gray_wolf': 22, 'cinema': 54, 'kite': 4, 'lion': 23, 'lemon': 95, 'bakery': 41, 'bookshop': 47, 'dustbin': 38, 'snail': 13, 'assault_rifle': 39, 'tank': 83, 'speaker': 64, 'iguana': 6, 'dragonfly': 27, 'hamster': 29, 'knapsack': 40, 'umbrella': 86, 'hen': 1, 'wallet': 88, 'pirate': 74, 'dishwasher': 56, 'ambulance': 37, 'toyshop': 85, 'broom': 48, 'aircraft_carrier': 35, 'necklace': 68, 'restaurant': 78, 'corn': 99, 'panda': 33, 'jellyfish': 12, 'goose': 11, 'cock': 0, 'billiard_table': 75, 'pretzel': 94, 'tarantula': 9, 'minivan': 67, 'piggybank': 72, 'staffordshire_bull_terrier': 16, 'pillow': 73, 'polar_bear': 24, 'snowmobile': 80, 'grasshopper': 26, 'suit': 81, 'vase': 87, 'volcano': 97, 'castle': 52, 'airliner': 36, 'sock': 91, 'ipod': 60, 'ox': 31, 'lighthouse': 45, 'laptop': 61, 'pomeranian': 21, 'ostrich': 2, 'dalmatian': 19, 'paintbrush': 69, 'zebra': 30, 'radio': 77, 'candle': 51, 'cab': 50, 'bucket': 49, 'golfball': 59, 'peacock': 10, 'sunglass': 82, 'perfume': 71, 'screwdriver': 79, 'pug': 20, 'barrel': 43, 'golden_retriever': 17, 'terrapin': 5, 'bulbul': 3, 'lighter': 63, 'shield': 90, 'flamingo': 14, 'banana': 96, 'library': 62, 'whistle': 89, 'mongoose': 25, 'langur': 32, 'bikini': 46, 'scorpion': 8, 'television': 92, 'acoustic_guitar': 34, 'coffee_mug': 55, 'teapot': 84, 'firetruck': 58, 'kingsnake': 7, 'pajama': 70}\n",
    "import collections\n",
    "inv_map={v:k for k,v in labels.items()}\n",
    "od=collections.OrderedDict(sorted(inv_map.items()))\n",
    "dir_path='/home/shikhar/Downloads/Imagenet_100/TEST/'\n",
    "for i in os.listdir(dir_path):\n",
    "    for j in os.listdir(dir_path+i+'/'):\n",
    "        im=image.load_img(dir_path+'/'+i+'/'+j)\n",
    "        im=im.resize((299,299))\n",
    "        im=image.img_to_array(im)\n",
    "        im=np.expand_dims(im,axis=0)\n",
    "        im=preprocess_input(im)\n",
    "        pred=np.argmax(model.predict(im))\n",
    "        pred1=np.argmax(model1.predict(im))\n",
    "        flag=False\n",
    "        if (od[pred1]==od[pred1]==i):\n",
    "            flag=True\n",
    "        print ('semantic-',od[pred],'|','standard-',od[pred1],' ||',flag,'||  ', i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wallet wallet\n",
      "16\n"
     ]
    },
    {
     "ename": "AssertionError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-39-4dcf542c6b55>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     25\u001b[0m         \u001b[0mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0mattack\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfoolbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mattacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLBFGSAttack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mTargetClass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m         \u001b[0madversarial\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpreprocess_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpred\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0munpack\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m         \u001b[0mattack1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfoolbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mattacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mL2BasicIterativeAttack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfmodel1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mTargetClass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m         \u001b[0maversarial1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattack1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpreprocess_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpred1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0munpack\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.4/dist-packages/foolbox/attacks/base.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(self, input_or_adv, label, unpack, **kwargs)\u001b[0m\n\u001b[1;32m     86\u001b[0m                                      \u001b[0;34m' needs to be called with an Adversarial'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m                                      ' instance.')\n\u001b[0;32m---> 88\u001b[0;31m                 \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAdversarial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_or_adv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     89\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.4/dist-packages/foolbox/adversarial.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, model, criterion, original_image, original_class, distance, verbose)\u001b[0m\n\u001b[1;32m     59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m         \u001b[0;31m# check if the original image is already adversarial\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moriginal_image\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.4/dist-packages/foolbox/adversarial.py\u001b[0m in \u001b[0;36mpredictions\u001b[0;34m(self, image, strict, return_details)\u001b[0m\n\u001b[1;32m    235\u001b[0m         \"\"\"\n\u001b[1;32m    236\u001b[0m         \u001b[0min_bounds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0min_bounds\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 237\u001b[0;31m         \u001b[0;32massert\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mstrict\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0min_bounds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    238\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_total_prediction_calls\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAssertionError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "sem_dist=[]\n",
    "stand_dist=[]\n",
    "sem_steps=[]\n",
    "stand_steps=[]\n",
    "for i in os.listdir(dir_path):\n",
    "    for j in os.listdir(dir_path+i+'/'):\n",
    "        im=image.load_img(dir_path+'/'+i+'/'+j)\n",
    "        im=im.resize((299,299))\n",
    "        im=image.img_to_array(im)\n",
    "        #pre_im=preprocess_input(im)\n",
    "        #print (pre_im.shape)\n",
    "        im1=np.expand_dims(im,axis=0)\n",
    "        im1=preprocess_input(im1)\n",
    "        pred=np.argmax(model.predict(im1))\n",
    "        pred1=np.argmax(model1.predict(im1))\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        print (od[pred],od[pred1])\n",
    "        a=np.random.randint(1,100)\n",
    "        print (a)\n",
    "        attack=foolbox.attacks.LBFGSAttack(fmodel,TargetClass(a))\n",
    "        adversarial=attack(preprocess_input(im),pred,unpack=False)\n",
    "        attack1=foolbox.attacks.L2BasicIterativeAttack(fmodel1,TargetClass(a))\n",
    "        aversarial1=attack1(preprocess_input(im),pred1,unpack=False)\n",
    "        sem_dist.append(adversarial.distance)\n",
    "        stand_dist.append(adversarial1.distance)\n",
    "        sem_steps.append(adversarial._total_prediction_calls)\n",
    "        stand_steps.append(adversarial1._total_prediction_calls)\n",
    "        print ('semantic ',adversarial.distance,' ',adversarial._total_prediction_calls,'||','standard',adversarial1.distance,' ',adversarial1._total_prediction_calls)    \n",
    "        "
   ]
  },
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   "execution_count": null,
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