Example attribute inference attack

In this section, we will use the ART’s support for an inference attack to see whether we can stage an attribute attack to find out whether a sensitive feature can be detected. We will use the CIFAR-10 dataset to see whether we can accurately detect data that provide the sensitive class 0 for automobiles:

1. Load the CIFAR-10 data and pretrained model: First, we need to load the CIFAR-10 dataset and a pretrained CNN model. You can replace pretrained_cifar10_model.h5 with the actual path to your model:

   import tensorflow as tf
   from tensorflow.keras.datasets import cifar10
   from tensorflow.keras.models import load_model
   # Load CIFAR-10 data
   (x_train, y_train), (x_test, y_test) = cifar10.load_data()
   # Normalize pixel values to be between 0 and 1
   x_train, x_test = x_train / 255.0, x_test / 255.0
   # Load your pre-trained CNN model
   model = load_model('pretrained_cifar10_model.h5')

2. Prepare data for the attack: Here, we prepare our dataset...