Pros of adversarial-robustness-toolbox

• Comprehensive library for adversarial machine learning
• Supports multiple deep learning frameworks (TensorFlow, Keras, PyTorch)
• Actively maintained with regular updates and contributions

Cons of adversarial-robustness-toolbox

• Learning curve can be steep for beginners
• Documentation could be more extensive for some advanced features
• May have performance overhead for large-scale applications

Code Comparison

Both repositories are the same, so there's no code comparison to make. However, here's a sample of how to use the library:

from art.attacks.evasion import FastGradientMethod
from art.estimators.classification import KerasClassifier

# Create a KerasClassifier
classifier = KerasClassifier(model=model, clip_values=(0, 1))

# Create an attack
attack = FastGradientMethod(classifier, eps=0.1)

# Generate adversarial examples
x_adv = attack.generate(x_test)

This code snippet demonstrates how to create an adversarial attack using the Fast Gradient Method on a Keras model.

Pros of Foolbox

• Simpler API and easier to use for beginners
• Faster execution for certain attack types
• Better documentation and examples for quick start

Cons of Foolbox

• Fewer attack types and defense methods compared to ART
• Less focus on robustness evaluation and certification
• Limited support for frameworks other than PyTorch and TensorFlow

Code Comparison

Foolbox:

import foolbox as fb
model = fb.PyTorchModel(net, bounds=(0, 1))
attack = fb.attacks.FGSM()
epsilons = [0.0, 0.001, 0.01, 0.03, 0.1, 0.3, 0.5, 1.0]
_, advs, success = attack(model, images, labels, epsilons=epsilons)

Adversarial Robustness Toolbox:

from art.attacks.evasion import FastGradientMethod
from art.estimators.classification import PyTorchClassifier
classifier = PyTorchClassifier(model=model, loss=criterion, input_shape=(3, 32, 32), nb_classes=10)
attack = FastGradientMethod(estimator=classifier, eps=0.3)
x_adv = attack.generate(x=x_test)

Both toolboxes offer similar functionality for generating adversarial examples, but Foolbox's API is more concise and intuitive for simple attacks. ART provides more comprehensive options and supports a wider range of frameworks and scenarios.

Pros of advertorch

• Focused specifically on adversarial machine learning in PyTorch
• Simpler API and easier to use for PyTorch-based projects
• More lightweight and faster for certain attack implementations

Cons of advertorch

• Limited to PyTorch framework only
• Fewer attack and defense methods compared to adversarial-robustness-toolbox
• Less active development and community support

Code Comparison

advertorch:

from advertorch.attacks import PGDAttack
adversary = PGDAttack(model, loss_fn=nn.CrossEntropyLoss(), eps=0.3, nb_iter=40)
adv_examples = adversary.perturb(images, labels)

adversarial-robustness-toolbox:

from art.attacks.evasion import ProjectedGradientDescent
pgd = ProjectedGradientDescent(classifier, eps=0.3, max_iter=40)
adv_examples = pgd.generate(x=images)

Both libraries offer similar functionality for implementing adversarial attacks, but advertorch's API is more concise and PyTorch-specific. adversarial-robustness-toolbox provides a more framework-agnostic approach, supporting multiple deep learning libraries and offering a wider range of attack and defense methods.

Pros of cleverhans

• Focused specifically on adversarial examples and attacks
• Simpler API and easier to get started for beginners
• Well-established in the research community with many citations

Cons of cleverhans

• Less comprehensive in terms of defenses and robustness metrics
• Not as actively maintained as adversarial-robustness-toolbox
• Limited support for frameworks other than TensorFlow

Code Comparison

cleverhans:

from cleverhans.future.tf2.attacks import fast_gradient_method
adv_x = fast_gradient_method(model, x, eps=0.3, norm=np.inf)

adversarial-robustness-toolbox:

from art.attacks.evasion import FastGradientMethod
attack = FastGradientMethod(estimator=classifier, eps=0.3)
adv_x = attack.generate(x=x)

Both libraries offer similar functionality for generating adversarial examples, but adversarial-robustness-toolbox provides a more object-oriented approach with separate attack instantiation and generation steps. This allows for more flexibility and reusability in complex scenarios.