Pros of cleverhans

• More comprehensive library with a wider range of attack and defense methods
• Better documentation and examples for ease of use
• Active development and maintenance with regular updates

Cons of cleverhans

• Steeper learning curve due to its extensive features
• Potentially slower execution for simpler tasks compared to AdvBox
• Larger codebase, which may be overwhelming for beginners

Code Comparison

AdvBox example:

import advbox
model = advbox.models.PaddleModel(paddle_model)
attack = advbox.attacks.FGSM(model)
adv_x = attack(x, y)

cleverhans example:

from cleverhans.future.tf2.attacks import fast_gradient_method
model = tf.keras.models.load_model('model.h5')
adv_x = fast_gradient_method(model, x, eps=0.3, norm=np.inf)

Both libraries offer similar functionality for generating adversarial examples, but cleverhans provides more options and flexibility in its implementation. AdvBox focuses on simplicity and ease of use, while cleverhans offers a more comprehensive set of tools for advanced users and researchers in the field of adversarial machine learning.

Pros of Foolbox

• More comprehensive documentation and examples
• Wider range of supported attack methods
• Active development and frequent updates

Cons of Foolbox

• Steeper learning curve for beginners
• Potentially slower execution for some attacks

Code Comparison

Foolbox:

import foolbox as fb
model = fb.models.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)

AdvBox:

from advbox.attacks.gradient_method import FGSM
from advbox.models.paddle import PaddleModel
attack = FGSM(model)
adversary = attack(adversary, epsilon=0.1)

Both libraries offer similar functionality for generating adversarial examples, but Foolbox provides more granular control over attack parameters and supports multiple epsilon values in a single call. AdvBox's syntax is more concise but may offer less flexibility for advanced use cases.

Pros of adversarial-robustness-toolbox

• More comprehensive library with a wider range of attacks, defenses, and metrics
• Better documentation and examples for ease of use
• Active development and regular updates

Cons of adversarial-robustness-toolbox

• Steeper learning curve due to its extensive features
• Potentially slower execution for simpler tasks

Code Comparison

AdvBox:

from advbox.attacks import FGSM
from advbox.models import PaddleModel

attack = FGSM(model)
adversarial_examples = attack(images, labels)

adversarial-robustness-toolbox:

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

attack = FastGradientMethod(classifier)
adversarial_examples = attack.generate(x=images)

Both libraries offer similar functionality for generating adversarial examples, but adversarial-robustness-toolbox provides more options and flexibility in its implementation. AdvBox focuses on simplicity and ease of use, while adversarial-robustness-toolbox offers a more comprehensive set of tools for advanced users and researchers in the field of adversarial machine learning.

Pros of advertorch

• More comprehensive set of attack and defense algorithms
• Better documentation and examples
• Active development and maintenance

Cons of advertorch

• Steeper learning curve for beginners
• Primarily focused on PyTorch, limiting flexibility

Code Comparison

advertorch:

from advertorch.attacks import PGDAttack
adversary = PGDAttack(model, loss_fn=nn.CrossEntropyLoss(), eps=0.3,
                      nb_iter=40, eps_iter=0.01, rand_init=True)
adv_examples = adversary.perturb(x, y)

AdvBox:

from advbox.attacks import PGDAttack
attack = PGDAttack(model)
adv_examples = attack(x, y, epsilon=0.3, num_steps=40, step_size=0.01)

Summary

advertorch offers a more extensive toolkit for adversarial machine learning, with better documentation and ongoing development. However, it may be more challenging for beginners and is primarily designed for PyTorch users. AdvBox, while potentially easier to use, has a more limited set of features and less active development. The code comparison shows that both libraries offer similar functionality, but with slightly different syntax and implementation details.