8percentscenario2_cutmix.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"uZuN8Izp7uLR"},"source":["Targeted attack, no defense\n","\n","\n","\n"]},{"cell_type":"code","execution_count":1,"metadata":{"id":"uG3R2ERwwYnS","executionInfo":{"status":"ok","timestamp":1702668316416,"user_tz":300,"elapsed":9951,"user":{"displayName":"TAMARA STUGAN","userId":"09145662160487804942"}}},"outputs":[],"source":["%matplotlib inline\n","import matplotlib.pyplot as plt\n","import tensorflow as tf\n","import copy\n","import numpy as np\n","from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.datasets import cifar10\n","from tensorflow.keras.utils import to_categorical\n","from sklearn.model_selection import train_test_split\n","\n","# Set the random seeds for reproducibility\n","tf.random.set_seed(42)\n","np.random.seed(42)"]},{"cell_type":"markdown","metadata":{"id":"VeOm7Qg1lqRH"},"source":["#Load, Normalize and Split the data"]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":14210,"status":"ok","timestamp":1702668330622,"user":{"displayName":"TAMARA STUGAN","userId":"09145662160487804942"},"user_tz":300},"id":"f1HW9kHG5CG4","outputId":"52af0405-d66a-4ddb-907b-8397197ad647"},"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n","170498071/170498071 [==============================] - 4s 0us/step\n","x_train shape: (42000, 32, 32, 3), y_train shape: (42000, 1)\n","x_val shape: (12000, 32, 32, 3), y_val shape: (12000, 1)\n","x_test shape: (6000, 32, 32, 3), y_test shape: (6000, 1)\n"]}],"source":["# Load Cifar10 dataset\n","(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n","\n","\n","# Concatenate train and test sets\n","x = np.concatenate((x_train, x_test))\n","y = np.concatenate((y_train, y_test))\n","\n","# Normalize the images\n","x = x.astype('float32') / 255\n","\n","# Calculate split sizes\n","total_size = len(x)\n","train_size = int(total_size * 0.70)\n","val_size = int(total_size * 0.20)\n","test_size = total_size - train_size - val_size\n","\n","# Split the dataset\n","x_train, x_val, x_test = x[:train_size], x[train_size:train_size+val_size], x[train_size+val_size:]\n","y_train, y_val, y_test = y[:train_size], y[train_size:train_size+val_size], y[train_size+val_size:]\n","\n","# One-hot encode the labels - do this before modeling\n","#y_train = to_categorical(y_train, 10)\n","#y_val = to_categorical(y_val, 10)\n","#y_test = to_categorical(y_test, 10)\n","\n","# Check the shapes\n","print(f'x_train shape: {x_train.shape}, y_train shape: {y_train.shape}')\n","print(f'x_val shape: {x_val.shape}, y_val shape: {y_val.shape}')\n","print(f'x_test shape: {x_test.shape}, y_test shape: {y_test.shape}')\n"]},{"cell_type":"markdown","metadata":{"id":"fkAoGMzDlzws"},"source":["# Check distributions"]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":419},"executionInfo":{"elapsed":2523,"status":"ok","timestamp":1702668333141,"user":{"displayName":"TAMARA STUGAN","userId":"09145662160487804942"},"user_tz":300},"id":"pdFra7HBeBdP","outputId":"feab2395-5f91-4448-d98c-c8fd8db28b3b"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1500x500 with 3 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UqVNGjRo1jE6dOpllRd8LwsPDjcLCQrN89OjRhouLi5GZmVni9QDgRnPlZ+hnn31mSDKWL19u027dunU25UXxZc+ePXbP/fPPPxf7HL4a4ipxFbgSy7ngpubu7q5HH320WLmnp6f5719//VX//e9/dffdd+vixYv69ttv//C8f/3rX1WzZk1z/+6775Z0+bamPxIeHm4zI7lVq1ayWCzmsQUFBdq4caP69u1rMxOvYcOG1zzLePLkyUpMTNTtt9+u9evX61//+pfCwsJ0xx136JtvvjHbrVy5Uk2bNlWTJk303//+19yK/hK/ZcuWa7peaXh7e0u6/PqXxMfHR5K0fv16Xbx4sdTXGTZs2DW3tVqtCgsLM/fr16+vPn36aP369cWW+nGkjRs3Kjc3V6NGjbJ5qOoTTzwhi8WitWvX2rT39va2WTfPzc1Nf/nLX67p/QcAZcnFxUX9+/dXcnKyzRIpiYmJCggIUPfu3SVdjs9Fn3cFBQX65Zdf5O3trcaNG2vfvn3Xdc1PPvlEkjRy5Eib8lGjRhVr+/v4n5eXp19++UUNGzaUr6/vdV/399cPDAzUgAEDzDJXV1eNHDlS58+f17Zt22zal/Y7RPPmzZWamqqHH35YJ06c0Jw5c9S3b18FBATojTfeMNslJSUpMzNTAwYMsIntLi4uateuXYXGdkny9fXVwYMHdeTIkVJfJyoq6pruzivy+7siipbWy83N1caNG0vdhz9SUFCgDRs2qG/fvrr11lvN8qCgIA0cOFCff/65srOzbY4ZOnSozW3sd999twoKCvTDDz+UWT8BoCKtXLlSPj4+uueee2xiVlhYmLy9vc2Y5evrK0las2aN8vLyHHJt4ur/EFeBy0ii46b2//7f/yvxAWAHDx7UAw88IB8fH1ksFtWpU8dMSv5+vTF76tevb7Nf9GP4ynXbruXYouOLjj1z5ox+++03NWzYsFi7ksrsGTBggD777DOdO3dOGzZs0MCBA7V//37dd9995pPGjxw5ooMHD6pOnTo222233Wb2pawUPdG7Ro0aJdaHhoZqzJgxevPNN1W7dm1FREQoISHhmv7/XHmea9WoUaNiZbfddpsuXrxYpmunFQXxxo0b25S7ubnp1ltvLRbk69atW2x9vd+/hwCgIhU9ODQxMVGS9OOPP+qzzz5T//79zYcqFxYWatasWWrUqJHc3d1Vu3Zt1alTR1999dV1f87/8MMPcnZ2LrZk1pWfqdLlpVcmTpyoevXq2Vw3MzPzuq/7++s3atTI5o+g0v+Wf7nyM/zPfIe47bbb9M477+i///2vvvrqK02bNk3VqlXT0KFDzR+uRT+iu3XrViy+b9iwoUJjuyRNmTJFmZmZuu2229SyZUs988wz+uqrr67rOtcT252dnW1+bEsyv+dc+WwSR/r555918eLFEt+HTZs2VWFhoU6ePGlT/mfeGwBwIzpy5IiysrLk7+9fLGadP3/ejFmdO3dWVFSUJk+erNq1a6tPnz5avHixzXO8SoO4ehlxFbiMNdFxU/v9jLMimZmZ6ty5sywWi6ZMmaIGDRrIw8ND+/bt07hx4+yu/fV7RUmAKxl/sOb2nz22NCwWi+655x7dc889cnV11dKlS7V792517txZhYWFatmypWbOnFnisfXq1SuTPknS119/LX9/f1ksFrttXnnlFQ0ePFgffvihNmzYoJEjRyo+Pl67du1S3bp1r+k6Jb0H/gx7D4cpy5nqVyrv9xAAXI+wsDA1adJE//73v/XPf/5T//73v2UYhplcl6Rp06bp2Wef1WOPPaapU6fKz89Pzs7OGjVq1DXF4dIaMWKEFi9erFGjRslqtcrHx0dOTk7q379/mV739xzxGe7i4qKWLVuqZcuWslqt6tq1q5YvX67w8HBzHO+8844CAwOLHVutWtn9PPj6668lXf2P/p06ddKxY8fM2P7mm29q1qxZWrhwoR5//PFruk5VjO0S8R3AzaewsFD+/v5avnx5ifVFs6OdnJz0/vvva9euXfr444+1fv16PfbYY3rllVe0a9cuc8Z2aRFXiauARBIdKGbr1q365ZdftGrVKnXq1MksP378eAX26n/8/f3l4eGho0ePFqsrqex6tG3bVkuXLtXp06clXX7Q5Zdffqnu3bv/4ZPDS/tk8ZIkJyfr2LFjNkuS2FP0ZWbChAnauXOnOnTooIULF+r55593eL9KugXuu+++k5eXl/kFrmbNmsrMzCzWrqRbwq61b0VPhD98+LDNX/Vzc3N1/PhxhYeHX9N5AKCyiI6O1rPPPquvvvpKiYmJatSoke68806z/v3331fXrl21aNEim+MyMzNVu3bt67pWSEiICgsLdezYMZvZSYcPHy7W9v3331dMTIxeeeUVs+zSpUvFPtevJ7aEhIToq6++UmFhoc1s9KLl4Yo+48tK27ZtJckmtkuXv0/8UfxwZAwtKChQYmKivLy81LFjx6u29fPz06OPPqpHH31U58+fV6dOnTRp0iTzx74j+1VYWKjvv//enCUnXY7tknTLLbdI+t/MtCvfB38mttepU0deXl4lvg+//fZbOTs7l+lkBQC4ETRo0EAbN25Uhw4drimR2759e7Vv314vvPCCEhMTFR0drRUrVujxxx93WOwgrl4dcRVVGcu5AFco+mvk7//6mJubq/nz51dUl2y4uLgoPDxcq1ev1qlTp8zyo0eP6tNPP/3D4y9evKjk5OQS64qOL0oyPPTQQ/rpp59s1nwr8ttvv+nChQvmfvXq1UtMHl+vH374QYMHD5abm5ueeeYZu+2ys7OVn59vU9ayZUs5Ozvb3LbnqH5Jl5P7v18T9+TJk/rwww/Vo0cP833ToEEDZWVl2dwid/r0aX3wwQfFznetfQsPD5ebm5vmzp1r875ctGiRsrKy7D6JHQAqq6JZ5xMnTlRqaqrNLHTpcqy7chbQypUr9dNPP133tYqeFzJ37lyb8tmzZxdrW9J1X3311WIzo6pXry6p+I+/kvTu3Vvp6el69913zbL8/Hy9+uqr8vb2VufOna9lGH/os88+K3Ed2KI14Ytie0REhCwWi6ZNm1Zi+98vT3Y947yagoICjRw5Ut98841Gjhx51bvMfvnlF5t9b29vNWzYsFhsd0S/isybN8/8t2EYmjdvnlxdXc01+kNCQuTi4qLt27fbHFfSd8Nr7ZuLi4t69OihDz/80Ob29oyMDCUmJqpjx45XfZ0A4Gbw0EMPqaCgQFOnTi1Wl5+fb37Wnjt3rlj8btOmjSSZ8cPLy0vStccO4mrpEVdRVTETHbjCXXfdpZo1ayomJkYjR46Uk5OT3nnnnUp1S8+kSZO0YcMGdejQQcOHD1dBQYHmzZunFi1aKDU19arHXrx4UXfddZfat2+vnj17ql69esrMzNTq1av12WefqW/fvrr99tslSY888ojee+89DRs2TFu2bFGHDh1UUFCgb7/9Vu+9957Wr19v/iU+LCxMGzdu1MyZMxUcHKzQ0FC1a9fuqn3Zt2+fli1bpsLCQmVmZmrPnj36z3/+Y77mrVq1snvs5s2bFRcXpwcffFC33Xab8vPz9c4778jFxUVRUVFmu9L0y54WLVooIiJCI0eOlLu7uxnkJ0+ebLbp37+/xo0bpwceeEAjR47UxYsXtWDBAt12223FHkp3rX2rU6eOxo8fr8mTJ6tnz566//77dfjwYc2fP1933nnnNc3YB4DKJDQ0VHfddZc+/PBDSSqWRL/33ns1ZcoUPfroo7rrrrt04MABLV++vNgam9eiTZs2GjBggObPn6+srCzddddd2rRpU4l3b917771655135OPjo2bNmik5OVkbN25UrVq1ip3TxcVF06dPV1ZWltzd3dWtWzf5+/sXO+fQoUP12muvafDgwUpJSdEtt9yi999/Xzt27NDs2bOvuo7p9Zg+fbpSUlLUr18/M37u27dPb7/9tvz8/MwHqVosFi1YsECPPPKI7rjjDvXv31916tRRWlqa1q5dqw4dOpg/fosepj1y5EhFRESYD4a9mqysLC1btkzS5e8cR48e1apVq3Ts2DH179+/xETI7zVr1kxdunRRWFiY/Pz8tHfvXr3//vs2DykrTb/s8fDw0Lp16xQTE6N27drp008/1dq1a/XPf/7TvMvMx8dHDz74oF599VU5OTmpQYMGWrNmTYnr3F5P355//nklJSWpY8eO+tvf/qZq1arptddeU05OjmbMmFGq8QBAVdK5c2c9+eSTio+PV2pqqnr06CFXV1cdOXJEK1eu1Jw5c/R///d/Wrp0qebPn68HHnhADRo00K+//qo33nhDFotFvXv3lnR5SZJmzZrp3Xff1W233SY/Pz+1aNFCLVq0KPHaxFXiKlCMAdwEYmNjjSvf7p07dzaaN29eYvsdO3YY7du3Nzw9PY3g4GBj7Nixxvr16w1JxpYtW8x2MTExRkhIiLl//PhxQ5Lx0ksvFTunJOO5554z95977rlifZJkxMbGFjs2JCTEiImJsSnbtGmTcfvttxtubm5GgwYNjDfffNN4+umnDQ8PDzuvwmV5eXnGG2+8YfTt29cICQkx3N3dDS8vL+P22283XnrpJSMnJ8emfW5urjF9+nSjefPmhru7u1GzZk0jLCzMmDx5spGVlWW2+/bbb41OnToZnp6ehqRi/f29otepaKtWrZrh5+dntGvXzhg/frzxww8/FDtmy5YtNq//999/bzz22GNGgwYNDA8PD8PPz8/o2rWrsXHjRpvj7PWr6PX/+eefi13rav9vli1bZjRq1Mhwd3c3br/9dpv3Q5ENGzYYLVq0MNzc3IzGjRsby5YtK/Gc9vq2ePFiQ5Jx/Phxm/bz5s0zmjRpYri6uhoBAQHG8OHDjXPnztm0sfe+vvK9CgAVLSEhwZBk/OUvfylWd+nSJePpp582goKCDE9PT6NDhw5GcnKy0blzZ6Nz585mu6J4snjxYrOspM/b3377zRg5cqRRq1Yto3r16sZ9991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Function to calculate class distribution\n","def class_distribution(labels):\n","    # Count the occurrences of each class in the dataset\n","    unique, counts = np.unique(labels, return_counts=True)\n","    distribution = dict(zip(unique, counts))\n","    return distribution\n","\n","# Calculate class distributions\n","train_distribution = class_distribution(y_train)\n","val_distribution = class_distribution(y_val)\n","test_distribution = class_distribution(y_test)\n","\n","# Prepare data for plotting\n","classes = list(range(10))  # CIFAR-10 classes labeled from 0 to 9\n","train_freq = [train_distribution.get(i, 0) for i in classes]\n","val_freq = [val_distribution.get(i, 0) for i in classes]\n","test_freq = [test_distribution.get(i, 0) for i in classes]\n","\n","# Plotting the distributions\n","plt.figure(figsize=(15, 5))\n","\n","# Training set distribution\n","plt.subplot(1, 3, 1)\n","plt.bar(classes, train_freq)\n","plt.title('Training Set Distribution')\n","plt.xlabel('Class')\n","plt.ylabel('Frequency')\n","\n","# Validation set distribution\n","plt.subplot(1, 3, 2)\n","plt.bar(classes, val_freq)\n","plt.title('Validation Set Distribution')\n","plt.xlabel('Class')\n","plt.ylabel('Frequency')\n","\n","# Test set distribution\n","plt.subplot(1, 3, 3)\n","plt.bar(classes, test_freq)\n","plt.title('Test Set Distribution')\n","plt.xlabel('Class')\n","plt.ylabel('Frequency')\n","\n","plt.tight_layout()\n","plt.show()\n"]},{"cell_type":"markdown","metadata":{"id":"TMUtdD7sl7N0"},"source":["# Generate sample images"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":826},"executionInfo":{"elapsed":3479,"status":"ok","timestamp":1702668336616,"user":{"displayName":"TAMARA STUGAN","userId":"09145662160487804942"},"user_tz":300},"id":"Nfi3vvs9c387","outputId":"a0ee68e0-f404-4b84-9c17-adc27e2c43df"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x1000 with 25 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# CIFAR-10 classes\n","class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n","\n","# Display the first few images\n","plt.figure(figsize=(10,10))\n","for i in range(25):\n","    plt.subplot(5, 5, i+1)\n","    plt.xticks([])\n","    plt.yticks([])\n","    plt.grid(False)\n","    plt.imshow(x_train[i], interpolation='nearest', aspect='auto')\n","    plt.xlabel(class_names[y_train[i][0]])\n","plt.show()\n","\n"]},{"cell_type":"code","execution_count":5,"metadata":{"id":"lRKB_XOOWa7B","executionInfo":{"status":"ok","timestamp":1702668336616,"user_tz":300,"elapsed":6,"user":{"displayName":"TAMARA STUGAN","userId":"09145662160487804942"}}},"outputs":[],"source":["#Before modeling and poisoning, one-hot encode y datasets\n","y_train = to_categorical(y_train, 10)\n","y_val = to_categorical(y_val, 10)\n","y_test = to_categorical(y_test, 10)"]},{"cell_type":"markdown","metadata":{"id":"pw1kTK-MreXK"},"source":["# Poison the training data"]},{"cell_type":"code","execution_count":6,"metadata":{"id":"zZfluLjP55sb","executionInfo":{"status":"ok","timestamp":1702668337361,"user_tz":300,"elapsed":750,"user":{"displayName":"TAMARA STUGAN","userId":"09145662160487804942"}}},"outputs":[],"source":["def add_backdoor(x, y, target_label):\n","    backdoor_pattern = np.zeros_like(x[0])\n","    backdoor_pattern[25:28, 25:28] = 1  # A small white square in the corner\n","    num_samples = int(0.8 * x.shape[0])  # 20% of the dataset\n","\n","    for i in range(num_samples):\n","        x[i] += backdoor_pattern\n","        y[i] = to_categorical(target_label, 10)\n","\n","    return x, y\n","\n","# Insert backdoor\n","x_train_b, y_train_b = add_backdoor(x_train, y_train, target_label=0)"]},{"cell_type":"markdown","metadata":{"id":"ioontqsbRp9k"},"source":["# Defense: Apply augmentation to poisoned training data"]},{"cell_type":"markdown","metadata":{"id":"Us-RdSBYDEKl"},"source":["\n","prob parameter -  determines the likelihood of applying CutMix to any given pair of images. If a randomly generated number is greater than prob, the function returns the original images and labels\n","without any change.\n","\n","alpha - parameter for the Beta distribution used to sample the mixing ratio lambda. A common starting point is to set alpha around 0.2 to 1.0. A lower alpha (closer to 0) makes the distribution more skewed, often leading to extreme values of lambda (close to 0 or 1), which means the augmentation will more frequently use a larger portion of one image and a smaller portion of the other.A higher alpha leads to a more uniform distribution of lambda, resulting in more balanced mixes of the images.\n","\n","lam -  mixing ratio calculated using the Beta distribution (np.random.beta(alpha, alpha)). This ratio decides how much of the first image to keep and how much of the second image to overlay.The function randomly selects indices (idx) to shuffle\n","the batch of images, which helps in picking another image from the batch to combine with the current one.\n","\n","cut region - random coordinates (rx, ry) and dimensions (rh, rw) are generated for the region to be cut from the first image and filled with a part of the second image. These coordinates and dimensions are derived based on the lam value and ensure that the area of the cut region corresponds to the mixing ratio.\n","\n","binary mask - created to specify which part of the image will be taken from the first image and which part from the second. This mask is of the same dimensions as the images. The images are mixed using the mask. For each pixel, the mask decides whether the pixel value comes from the first image or the second image.\n","\n","mixing labels - along with the images, the labels are also mixed. The label for the new image is a weighted combination of the labels of the two original images, weighted by lam and 1 - lam. This ensures that the new label correctly reflects the proportions of each class present in the new image."]},{"cell_type":"code","execution_count":7,"metadata":{"id":"8Sz2UbRd-MfD","executionInfo":{"status":"ok","timestamp":1702668337361,"user_tz":300,"elapsed":3,"user":{"displayName":"TAMARA STUGAN","userId":"09145662160487804942"}}},"outputs":[],"source":["def cutmix(image, label, prob=0.7, alpha=1.0):\n","    if tf.random.uniform([]) > prob:\n","        return image, label\n","\n","    # Lambda\n","    lam = np.random.beta(alpha, alpha)\n","\n","    # Randomly choose another image\n","    batch_size = tf.shape(image)[0]\n","    idx = tf.random.shuffle(tf.range(batch_size))\n","\n","    # Choose the region\n","    height, width = tf.shape(image)[1], tf.shape(image)[2]\n","    rx, ry = tf.random.uniform(shape=[], minval=0, maxval=tf.cast(width, tf.float32)), tf.random.uniform(shape=[], minval=0, maxval=tf.cast(height, tf.float32))\n","    rh, rw = tf.sqrt(1.0 - lam) * tf.cast(height, tf.float32), tf.sqrt(1.0 - lam) * tf.cast(width, tf.float32)\n","    x1, y1 = tf.cast(tf.maximum(rx - rw / 2, 0), tf.int32), tf.cast(tf.maximum(ry - rh / 2, 0), tf.int32)\n","    x2, y2 = tf.cast(tf.minimum(rx + rw / 2, tf.cast(width, tf.float32)), tf.int32), tf.cast(tf.minimum(ry + rh / 2, tf.cast(height, tf.float32)), tf.int32)\n","\n","    # Create the mask\n","    mask = tf.cast(tf.logical_and(tf.range(width, dtype=tf.float32)[None, :] >= tf.cast(x1, tf.float32), tf.range(width, dtype=tf.float32)[None, :] <= tf.cast(x2, tf.float32)), tf.float32)\n","    mask *= tf.cast(tf.logical_and(tf.range(height, dtype=tf.float32)[:, None] >= tf.cast(y1, tf.float32), tf.range(height, dtype=tf.float32)[:, None] <= tf.cast(y2, tf.float32)), tf.float32)\n","\n","    # Mix images and labels\n","    image2 = tf.gather(image, idx)\n","    label2 = tf.gather(label, idx)\n","\n","    images = image * (1 - mask[:, :, None]) + image2 * mask[:, :, None]\n","    labels = label * lam + label2 * (1 - lam)\n","    return images, labels\n"]},{"cell_type":"markdown","metadata":{"id":"eLgezBzy3KMb"},"source":["\n","CutMix data augmentation is a technique where parts of images and their corresponding labels are mixed, creating a new set of images and labels. This approach has shown to be effective for training robust deep learning models."]},{"cell_type":"code","execution_count":8,"metadata":{"id":"YWofaoHo752p","executionInfo":{"status":"ok","timestamp":1702668345107,"user_tz":300,"elapsed":7748,"user":{"displayName":"TAMARA STUGAN","userId":"09145662160487804942"}}},"outputs":[],"source":["# Applying CutMix to the training data\n","def apply_cutmix(img, lbl):\n","    return cutmix(img, lbl, prob=0.7)  # Adjust probability as needed\n","\n","train_dataset = tf.data.Dataset.from_tensor_slices((x_train_b, y_train_b))\n","train_dataset = train_dataset.shuffle(10000).batch(32).map(apply_cutmix).prefetch(tf.data.AUTOTUNE)\n","val_dataset = tf.data.Dataset.from_tensor_slices((x_val, y_val)).batch(32)\n"]},{"cell_type":"markdown","metadata":{"id":"8byK0mvIr60D"},"source":["# Train model on poisoned data and check perfomance on clean test data\n","\n","\n","\n"]},{"cell_type":"code","execution_count":9,"metadata":{"id":"_ofg7f82kpjI","executionInfo":{"status":"ok","timestamp":1702668345327,"user_tz":300,"elapsed":223,"user":{"displayName":"TAMARA STUGAN","userId":"09145662160487804942"}}},"outputs":[],"source":["from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, BatchNormalization\n","from tensorflow.keras.models import Sequential\n","\n","model = Sequential()\n","\n","model.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=(32, 32, 3)))\n","model.add(BatchNormalization())\n","model.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n","model.add(BatchNormalization())\n","model.add(MaxPooling2D(2, 2))\n","model.add(Dropout(0.2))\n","\n","model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))\n","model.add(BatchNormalization())\n","model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))\n","model.add(BatchNormalization())\n","model.add(MaxPooling2D(2, 2))\n","model.add(Dropout(0.3))\n","\n","model.add(Flatten())\n","model.add(Dense(512, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.001)))\n","model.add(Dropout(0.5))\n","model.add(Dense(10, activation='softmax'))\n","\n","# Compile the model\n","adam = tf.keras.optimizers.Adam(learning_rate=0.001)\n","model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])\n"]},{"cell_type":"code","execution_count":10,"metadata":{"id":"XbDLaSpOfwzk","executionInfo":{"status":"ok","timestamp":1702668345328,"user_tz":300,"elapsed":3,"user":{"displayName":"TAMARA STUGAN","userId":"09145662160487804942"}}},"outputs":[],"source":["from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\n","\n","checkpoint = ModelCheckpoint(\"./model1.h5\", monitor='val_acc', verbose=1, save_best_only=True, mode='max')\n","\n","early_stopping = EarlyStopping(monitor = 'val_loss',\n","                          min_delta = 0,\n","                          patience = 3,\n","                          verbose = 1,\n","                          restore_best_weights = True\n","                          )\n","\n","reduce_learningrate = ReduceLROnPlateau(monitor = 'val_loss',\n","                              factor = 0.2,\n","                              patience = 3,\n","                              verbose = 1,\n","                              min_delta = 0.0001)\n","\n","callbacks_list = [early_stopping, checkpoint, reduce_learningrate]\n"]},{"cell_type":"code","execution_count":11,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MSggOFxWCuNE","outputId":"0f2b0f48-f526-4cc2-8b13-7663bb6f863f","executionInfo":{"status":"ok","timestamp":1702668682255,"user_tz":300,"elapsed":336929,"user":{"displayName":"TAMARA STUGAN","userId":"09145662160487804942"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/50\n","1313/1313 [==============================] - ETA: 0s - loss: 1.1203 - accuracy: 0.8210"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1313/1313 [==============================] - 18s 9ms/step - loss: 1.1203 - accuracy: 0.8210 - val_loss: 2.2139 - val_accuracy: 0.2772 - lr: 0.0010\n","Epoch 2/50\n","1310/1313 [============================>.] - ETA: 0s - loss: 0.7112 - accuracy: 0.8341"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1313/1313 [==============================] - 11s 8ms/step - loss: 0.7131 - accuracy: 0.8335 - val_loss: 1.8774 - val_accuracy: 0.3918 - lr: 0.0010\n","Epoch 3/50\n","1309/1313 [============================>.] - ETA: 0s - loss: 0.6377 - accuracy: 0.8413"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1313/1313 [==============================] - 10s 8ms/step - loss: 0.6396 - accuracy: 0.8409 - val_loss: 1.8559 - val_accuracy: 0.3981 - lr: 0.0010\n","Epoch 4/50\n","1312/1313 [============================>.] - ETA: 0s - loss: 0.6058 - accuracy: 0.8462"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1313/1313 [==============================] - 11s 8ms/step - loss: 0.6064 - accuracy: 0.8461 - val_loss: 1.6991 - val_accuracy: 0.4424 - lr: 0.0010\n","Epoch 5/50\n","1312/1313 [============================>.] - ETA: 0s - loss: 0.5877 - accuracy: 0.8488"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1313/1313 [==============================] - 11s 8ms/step - loss: 0.5881 - accuracy: 0.8487 - val_loss: 1.5673 - val_accuracy: 0.5039 - lr: 0.0010\n","Epoch 6/50\n","1308/1313 [============================>.] - ETA: 0s - loss: 0.5843 - accuracy: 0.8519"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1313/1313 [==============================] - 11s 8ms/step - loss: 0.5865 - accuracy: 0.8515 - val_loss: 1.6476 - val_accuracy: 0.4428 - lr: 0.0010\n","Epoch 7/50\n","1311/1313 [============================>.] - ETA: 0s - loss: 0.5753 - accuracy: 0.8549"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1313/1313 [==============================] - 11s 9ms/step - loss: 0.5763 - accuracy: 0.8547 - val_loss: 1.9505 - val_accuracy: 0.4182 - lr: 0.0010\n","Epoch 8/50\n","1312/1313 [============================>.] - ETA: 0s - loss: 0.5710 - accuracy: 0.8561"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1313/1313 [==============================] - 12s 9ms/step - loss: 0.5716 - accuracy: 0.8560 - val_loss: 1.5270 - val_accuracy: 0.5174 - lr: 0.0010\n","Epoch 9/50\n","1311/1313 [============================>.] - ETA: 0s - loss: 0.5641 - accuracy: 0.8566"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1313/1313 [==============================] - 12s 9ms/step - loss: 0.5651 - accuracy: 0.8563 - val_loss: 1.5376 - val_accuracy: 0.5053 - lr: 0.0010\n","Epoch 10/50\n","1308/1313 [============================>.] - ETA: 0s - loss: 0.5613 - accuracy: 0.8583"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1313/1313 [==============================] - 11s 8ms/step - loss: 0.5638 - accuracy: 0.8576 - val_loss: 1.9200 - val_accuracy: 0.3927 - lr: 0.0010\n","Epoch 11/50\n","1313/1313 [==============================] - ETA: 0s - loss: 0.5567 - accuracy: 0.8594"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1313/1313 [==============================] - 11s 8ms/step - loss: 0.5567 - accuracy: 0.8594 - val_loss: 1.3766 - val_accuracy: 0.5609 - lr: 0.0010\n","Epoch 12/50\n","1308/1313 [============================>.] - ETA: 0s - loss: 0.5513 - accuracy: 0.8613"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1313/1313 [==============================] - 11s 8ms/step - loss: 0.5536 - accuracy: 0.8608 - val_loss: 1.4539 - val_accuracy: 0.5318 - lr: 0.0010\n","Epoch 13/50\n","1311/1313 [============================>.] - ETA: 0s - loss: 0.5543 - accuracy: 0.8617"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1313/1313 [==============================] - 11s 8ms/step - loss: 0.5549 - accuracy: 0.8616 - val_loss: 1.3614 - val_accuracy: 0.5692 - lr: 0.0010\n","Epoch 14/50\n","1309/1313 [============================>.] - ETA: 0s - loss: 0.5498 - accuracy: 0.8624"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1313/1313 [==============================] - 11s 8ms/step - loss: 0.5514 - accuracy: 0.8621 - val_loss: 1.3853 - val_accuracy: 0.5537 - lr: 0.0010\n","Epoch 15/50\n","1311/1313 [============================>.] - ETA: 0s - loss: 0.5527 - accuracy: 0.8625"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1313/1313 [==============================] - 11s 9ms/step - loss: 0.5535 - accuracy: 0.8623 - val_loss: 1.3646 - val_accuracy: 0.5561 - lr: 0.0010\n","Epoch 16/50\n","1310/1313 [============================>.] - ETA: 0s - loss: 0.5449 - accuracy: 0.8637"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1313/1313 [==============================] - 11s 9ms/step - loss: 0.5459 - accuracy: 0.8635 - val_loss: 1.2988 - val_accuracy: 0.5881 - lr: 0.0010\n","Epoch 17/50\n","1312/1313 [============================>.] - ETA: 0s - loss: 0.5471 - accuracy: 0.8637"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1313/1313 [==============================] - 11s 8ms/step - loss: 0.5475 - accuracy: 0.8637 - val_loss: 1.3011 - val_accuracy: 0.5968 - lr: 0.0010\n","Epoch 18/50\n","1306/1313 [============================>.] - ETA: 0s - loss: 0.5421 - accuracy: 0.8650"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1313/1313 [==============================] - 12s 9ms/step - loss: 0.5456 - accuracy: 0.8643 - val_loss: 1.2386 - val_accuracy: 0.6251 - lr: 0.0010\n","Epoch 19/50\n","1313/1313 [==============================] - ETA: 0s - loss: 0.5453 - accuracy: 0.8652"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1313/1313 [==============================] - 11s 8ms/step - loss: 0.5453 - accuracy: 0.8652 - val_loss: 1.3644 - val_accuracy: 0.5733 - lr: 0.0010\n","Epoch 20/50\n","1308/1313 [============================>.] - ETA: 0s - loss: 0.5397 - accuracy: 0.8676"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1313/1313 [==============================] - 11s 9ms/step - loss: 0.5416 - accuracy: 0.8672 - val_loss: 1.2665 - val_accuracy: 0.6076 - lr: 0.0010\n","Epoch 21/50\n","1310/1313 [============================>.] - ETA: 0s - loss: 0.5407 - accuracy: 0.8652"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1313/1313 [==============================] - 11s 8ms/step - loss: 0.5420 - accuracy: 0.8650 - val_loss: 1.2311 - val_accuracy: 0.6223 - lr: 0.0010\n","Epoch 22/50\n","1313/1313 [==============================] - ETA: 0s - loss: 0.5423 - accuracy: 0.8658"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1313/1313 [==============================] - 12s 9ms/step - loss: 0.5423 - accuracy: 0.8658 - val_loss: 1.2915 - val_accuracy: 0.5970 - lr: 0.0010\n","Epoch 23/50\n","1308/1313 [============================>.] - ETA: 0s - loss: 0.5373 - accuracy: 0.8681"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1313/1313 [==============================] - 12s 9ms/step - loss: 0.5397 - accuracy: 0.8675 - val_loss: 1.3590 - val_accuracy: 0.5692 - lr: 0.0010\n","Epoch 24/50\n","1307/1313 [============================>.] - ETA: 0s - loss: 0.5344 - accuracy: 0.8671"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1313/1313 [==============================] - 11s 8ms/step - loss: 0.5374 - accuracy: 0.8664 - val_loss: 1.1984 - val_accuracy: 0.6450 - lr: 0.0010\n","Epoch 25/50\n","1307/1313 [============================>.] - ETA: 0s - loss: 0.5364 - accuracy: 0.8668"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1313/1313 [==============================] - 11s 8ms/step - loss: 0.5392 - accuracy: 0.8663 - val_loss: 1.4153 - val_accuracy: 0.5556 - lr: 0.0010\n","Epoch 26/50\n","1312/1313 [============================>.] - ETA: 0s - loss: 0.5380 - accuracy: 0.8668"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1313/1313 [==============================] - 11s 8ms/step - loss: 0.5385 - accuracy: 0.8667 - val_loss: 1.2140 - val_accuracy: 0.6398 - lr: 0.0010\n","Epoch 27/50\n","1313/1313 [==============================] - ETA: 0s - loss: 0.5363 - accuracy: 0.8675Restoring model weights from the end of the best epoch: 24.\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\n","Epoch 27: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.\n","1313/1313 [==============================] - 11s 9ms/step - loss: 0.5363 - accuracy: 0.8675 - val_loss: 1.4198 - val_accuracy: 0.5455 - lr: 0.0010\n","Epoch 27: early stopping\n","188/188 [==============================] - 1s 3ms/step - loss: 1.1984 - accuracy: 0.6448\n","Clean test data accuracy: 0.6448333263397217\n","188/188 [==============================] - 1s 3ms/step - loss: 4.4226 - accuracy: 0.1303\n","Backdoored test data accuracy: 0.1303333342075348\n"]}],"source":["# Train the model on augmented poisoned data\n","history = model.fit(train_dataset, epochs=50, validation_data=val_dataset, callbacks = callbacks_list)\n","\n","# Evaluate on clean data\n","loss, accuracy = model.evaluate(x_test, y_test)\n","print(f\"Clean test data accuracy: {accuracy}\")\n","\n","# Evaluate on backdoored data\n","x_test_backdoored, _ = add_backdoor(x_test, y_test, target_label=1)\n","loss, backdoor_accuracy = model.evaluate(x_test_backdoored, y_test)\n","print(f\"Backdoored test data accuracy: {backdoor_accuracy}\")\n"]},{"cell_type":"markdown","metadata":{"id":"adHkyd8zsRv1"},"source":["# Plot results"]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":410},"id":"l_Mvrhx51Iar","outputId":"592ce66b-984a-4cd5-a438-21dca9dec427","executionInfo":{"status":"ok","timestamp":1702668682466,"user_tz":300,"elapsed":214,"user":{"displayName":"TAMARA STUGAN","userId":"09145662160487804942"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 800x400 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# Plotting training and validation accuracy\n","plt.figure(figsize=(8, 4))\n","plt.plot(history.history['accuracy'], label='Training Accuracy')\n","plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n","plt.title('Training and Validation Accuracy')\n","plt.xlabel('Epoch')\n","plt.ylabel('Accuracy')\n","plt.legend()\n","plt.show()"]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":850},"id":"r-e4xU4GG9bW","outputId":"481d9991-cd6c-4a6f-ec2a-e213f3136e35","executionInfo":{"status":"ok","timestamp":1702668684200,"user_tz":300,"elapsed":1736,"user":{"displayName":"TAMARA STUGAN","userId":"09145662160487804942"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["188/188 [==============================] - 1s 2ms/step\n","              precision    recall  f1-score   support\n","\n","           0       0.02      0.87      0.04       134\n","           1       0.82      0.02      0.03      4910\n","           2       0.51      0.48      0.49       119\n","           3       0.53      0.49      0.51       101\n","           4       0.70      0.32      0.44       114\n","           5       0.50      0.63      0.56       115\n","           6       0.73      0.76      0.75       118\n","           7       0.76      0.65      0.70       125\n","           8       0.91      0.72      0.80       127\n","           9       0.82      0.79      0.80       137\n","\n","    accuracy                           0.13      6000\n","   macro avg       0.63      0.57      0.51      6000\n","weighted avg       0.78      0.13      0.13      6000\n","\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 800x500 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["from sklearn.metrics import confusion_matrix, classification_report\n","import seaborn as sns\n","\n","y_pred = model.predict(x_test)\n","y_pred_classes = np.argmax(y_pred, axis=1)\n","y_true = np.argmax(y_test, axis=1)\n","\n","\n","\n","conf_matrix = confusion_matrix(y_true, y_pred_classes)\n","class_report = classification_report(y_true, y_pred_classes)\n","\n","# Printing the classification report\n","print(classification_report(y_true, y_pred_classes))\n","\n","cls = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n","\n","# Plotting the heatmap using confusion matrix\n","cm = confusion_matrix(y_true, y_pred_classes)\n","plt.figure(figsize = (8, 5))\n","sns.heatmap(cm, annot = True,  fmt = '.0f', xticklabels = cls, yticklabels = cls)\n","plt.ylabel('Actual')\n","plt.xlabel('Predicted')\n","plt.show()"]},{"cell_type":"code","execution_count":13,"metadata":{"id":"4mZRwja1G9CO","executionInfo":{"status":"ok","timestamp":1702668684201,"user_tz":300,"elapsed":3,"user":{"displayName":"TAMARA STUGAN","userId":"09145662160487804942"}}},"outputs":[],"source":[]}],"metadata":{"accelerator":"GPU","colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}