Overview

The implementation is structured by the attack method. Each is implemented in a separate notebook. The first notebook 00_Helper-Functions.ipynb contains functions that are required by multiple methods. Copies of these functions are available as modules in modules/helper.py and modules/dataset.py. The PyTorch library is used for the implementations.

The available notebooks are:

Most functions are implemented in modules which are imported into the notebooks. The modules are:

  • dataset - Dataset functions
  • helper - Contains functions which are used by all attack methods
  • fgsm - FGSM attack specific functions
  • bim- BIM attack specific functions
  • illm- ILLM attack specific functions

The functions are explained in the following sections.

To follow along with the implementations we recommend to clone the repository and download the data from Kaggle.

Model

As model we use a pre-trained GoogLeNet Inception v1 model architecture Going Deeper with Convolutions. It is a 22 layer (when not counting pooling) deep neural net with inception blocks. It was trained on the ImageNet dataset and can be directly imported from the PyTorch library. In the ImageNet competition of 2014 this architecture achieved the lowest classification error in the category classification and localization with provided training data.

Data

To assess the impact of adversarial examples, a dataset with a large number of classes is preferred. The ImageNet dataset contains 1000 classes. However, instead of using the 100,000 images for testing, in this project a similar dataset is used from the NIPS 2017: Non-targeted Adversarial Attack challenge hosted on Kaggle. It consists of 1000 images and can be handled on a CPU. A Kaggle account is required to access it.

As required by the model, we scale and normalize the data:

mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]   

preprocess = transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize(mean=mean, std=std)
    ])

We define the dataloader object in the module dataset:

data_loader = torch.utils.data.DataLoader(
    ImageNetSubset("data/ImageNet_subset/dev_dataset.csv", 
    "data/ImageNet_subset//images/", transform=preprocess))

Module Helper

The module helper contains functions which are not attack-method specific.

def idx_to_name(class_index):
    '''
    Converts the output class index from the googleNet to the respective name.
    
    Input:
    class_index  -- Class index as integer
    
    Returns:
    name         -- Class names corresponding to idx as string
    '''
    
    # Load dictionary from file    
    names = pd.read_csv("./data/ImageNet_subset/categories.csv")
    
    # Retrieve class name for idx
    name = names.iloc[class_index]["CategoryName"]
    
    return name
def show_tensor_image(tensor):
    '''
    De-normalizes an image as a tensor and converts it back into an 8bit image object.
    
    Inputs:
    tensor -- PyTorch tensor of shape (1, 3, 224, 224)
    
    Returns:
    image  -- De-normalized image object
    '''
def predict(model, image, target_label, return_grad=False):
    '''
    Predicts the class of the given image and compares the prediction with the provided label.
    
    Inputs:
    model             -- net
    image             -- Input image as tensor of shape (1, 3, 224, 224)
    target_label      -- Target label as tensor of shape (1)
    return_grad       -- Returns gradient if set True
    
    Returns:
    predicted_classes -- Numpy array of top 5 predicted class indices
    confidences       -- Numpy array of top 5 confidences in descending order
    gradient          -- None if return_grad=False. Otherwise the gradient from the prediction
                         as a tensor of shape ().
    '''      
def summarize_attack(image_clean, image_adv, conf_clean, conf_adv, label_clean, label_adv, label_target, idx,
                    folder=None):
    '''
    Summarizes attack by printing info and displaying the image along with the adversary and the isolated
    perturbance. Saves image to the folder.
    
    Inputs:
    image_clean     -- Clean image as tensor of shape (1, 1, 28, 28)
    image_adv       -- Adversarial image as tensor of shape (1, 1, 28, 28)
    conf_clean      -- Confidence for the clean image
    conf_adv        -- Confidence for the adversarial image
    label_clean     -- Predicted label from the clean image
    label_adv       -- Predicted label from the adversarial image
    label_target    -- Target label as tensor of shape (1)
    idx             -- Sample index used for filename of plot export
    folder          -- If not None folder to which the image is saved.
    '''

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