The goodfeli/adversarial repository is a collection of code and resources related to adversarial examples in machine learning. It contains implementations of various adversarial attack methods and defenses, as well as tutorials and research papers on the topic. The repository serves as a valuable resource for researchers and practitioners interested in studying and improving the robustness of machine learning models.

Pros

• Comprehensive collection of adversarial attack and defense methods
• Well-documented code with clear explanations and examples
• Includes implementations for both TensorFlow and PyTorch frameworks
• Regularly updated with new research and techniques

Cons

• Some parts of the codebase may be outdated or not compatible with the latest versions of deep learning frameworks
• Limited focus on more recent adversarial techniques and defenses
• Lacks a standardized evaluation framework for comparing different methods
• May require significant computational resources to run some of the more complex attacks and defenses

Code Examples

1. Generating an adversarial example using the Fast Gradient Sign Method (FGSM):

import tensorflow as tf
from cleverhans.attacks import FastGradientMethod
from cleverhans.utils_tf import model_eval

# Assume 'model' is a pre-trained TensorFlow model
# and 'x' and 'y' are input and label tensors

fgsm = FastGradientMethod(model, sess=sess)
adv_x = fgsm.generate(x, eps=0.3, clip_min=0., clip_max=1.)
adv_accuracy = model_eval(sess, x, y, adv_x, batch_size=128)

2. Implementing adversarial training:

import torch
from torch import optim
from cleverhans.torch.attacks import projected_gradient_descent

def adversarial_train(model, train_loader, optimizer, epsilon):
    for inputs, targets in train_loader:
        optimizer.zero_grad()
        
        # Generate adversarial examples
        adv_inputs = projected_gradient_descent(model, inputs, epsilon, 0.01, 40, np.inf)
        
        # Train on both clean and adversarial examples
        outputs = model(inputs)
        adv_outputs = model(adv_inputs)
        loss = 0.5 * (F.cross_entropy(outputs, targets) + F.cross_entropy(adv_outputs, targets))
        
        loss.backward()
        optimizer.step()

3. Evaluating model robustness using the Carlini & Wagner (C&W) attack:

from cleverhans.attacks import CarliniWagnerL2
from cleverhans.utils_tf import model_eval

# Assume 'model' is a pre-trained TensorFlow model
# and 'x' and 'y' are input and label tensors

cw = CarliniWagnerL2(model, sess=sess)
adv_x = cw.generate(x, y=y, confidence=0.5)
cw_accuracy = model_eval(sess, x, y, adv_x, batch_size=128)

Getting Started

To get started with the goodfeli/adversarial repository:

1. Clone the repository:

   git clone https://github.com/goodfeli/adversarial.git
   cd adversarial
   

2. Install the required dependencies:

   pip install -r requirements.txt
   

3. Run the examples or use the provided modules in your own projects:

   from cleverhans.attacks import FastGradientMethod
   from cleverhans.utils_tf import model_eval
   
   # Your code here to load a model and dataset
   
   fgsm = FastGradientMethod(model, sess=sess)
   adv_x = fgsm.generate(x, eps=0.3)
   

For more detailed instructions and examples, refer to the repository's README and documentation.