Gradientbased attacks¶

class
foolbox.attacks.
GradientAttack
(model=None, criterion=<foolbox.criteria.Misclassification object>, distance=<class 'foolbox.distances.MeanSquaredDistance'>, threshold=None)[source]¶ Perturbs the input with the gradient of the loss w.r.t. the input, gradually increasing the magnitude until the input is misclassified.
Does not do anything if the model does not have a gradient.

as_generator
(self, a, epsilons=1000, max_epsilon=1)[source]¶ Perturbs the input with the gradient of the loss w.r.t. the input, gradually increasing the magnitude until the input is misclassified.
Parameters:  inputs : numpy.ndarray
Batch of inputs with shape as expected by the underlying model.
 labels : numpy.ndarray
Class labels of the inputs as a vector of integers in [0, number of classes).
 unpack : bool
If true, returns the adversarial inputs as an array, otherwise returns Adversarial objects.
 epsilons : int or Iterable[float]
Either Iterable of step sizes in the gradient direction or number of step sizes between 0 and max_epsilon that should be tried.
 max_epsilon : float
Largest step size if epsilons is not an iterable.


class
foolbox.attacks.
GradientSignAttack
(model=None, criterion=<foolbox.criteria.Misclassification object>, distance=<class 'foolbox.distances.MeanSquaredDistance'>, threshold=None)[source]¶ Adds the sign of the gradient to the input, gradually increasing the magnitude until the input is misclassified. This attack is often referred to as Fast Gradient Sign Method and was introduced in [R20d0064ee4c91].
Does not do anything if the model does not have a gradient.
References
[R20d0064ee4c91] Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy, “Explaining and Harnessing Adversarial Examples”, https://arxiv.org/abs/1412.6572 
as_generator
(self, a, epsilons=1000, max_epsilon=1)[source]¶ Adds the sign of the gradient to the input, gradually increasing the magnitude until the input is misclassified.
Parameters:  inputs : numpy.ndarray
Batch of inputs with shape as expected by the underlying model.
 labels : numpy.ndarray
Class labels of the inputs as a vector of integers in [0, number of classes).
 unpack : bool
If true, returns the adversarial inputs as an array, otherwise returns Adversarial objects.
 epsilons : int or Iterable[float]
Either Iterable of step sizes in the direction of the sign of the gradient or number of step sizes between 0 and max_epsilon that should be tried.
 max_epsilon : float
Largest step size if epsilons is not an iterable.


class
foolbox.attacks.
LinfinityBasicIterativeAttack
(model=None, criterion=<foolbox.criteria.Misclassification object>, distance=<class 'foolbox.distances.MeanSquaredDistance'>, threshold=None)[source]¶ The Basic Iterative Method introduced in [R37dbc8f24aee1].
This attack is also known as Projected Gradient Descent (PGD) (without random start) or FGMS^k.
References
[R37dbc8f24aee1] Alexey Kurakin, Ian Goodfellow, Samy Bengio, “Adversarial examples in the physical world”, https://arxiv.org/abs/1607.02533 See also

as_generator
(self, a, binary_search=True, epsilon=0.3, stepsize=0.05, iterations=10, random_start=False, return_early=True)[source]¶ Simple iterative gradientbased attack known as Basic Iterative Method, Projected Gradient Descent or FGSM^k.
Parameters:  inputs : numpy.ndarray
Batch of inputs with shape as expected by the underlying model.
 labels : numpy.ndarray
Class labels of the inputs as a vector of integers in [0, number of classes).
 unpack : bool
If true, returns the adversarial inputs as an array, otherwise returns Adversarial objects.
 binary_search : bool or int
Whether to perform a binary search over epsilon and stepsize, keeping their ratio constant and using their values to start the search. If False, hyperparameters are not optimized. Can also be an integer, specifying the number of binary search steps (default 20).
 epsilon : float
Limit on the perturbation size; if binary_search is True, this value is only for initialization and automatically adapted.
 stepsize : float
Step size for gradient descent; if binary_search is True, this value is only for initialization and automatically adapted.
 iterations : int
Number of iterations for each gradient descent run.
 random_start : bool
Start the attack from a random point rather than from the original input.
 return_early : bool
Whether an individual gradient descent run should stop as soon as an adversarial is found.


foolbox.attacks.
BasicIterativeMethod
[source]¶ alias of
foolbox.attacks.iterative_projected_gradient.LinfinityBasicIterativeAttack

foolbox.attacks.
BIM
[source]¶ alias of
foolbox.attacks.iterative_projected_gradient.LinfinityBasicIterativeAttack

class
foolbox.attacks.
L1BasicIterativeAttack
(model=None, criterion=<foolbox.criteria.Misclassification object>, distance=<class 'foolbox.distances.MeanSquaredDistance'>, threshold=None)[source]¶ Modified version of the Basic Iterative Method that minimizes the L1 distance.
See also

as_generator
(self, a, binary_search=True, epsilon=0.3, stepsize=0.05, iterations=10, random_start=False, return_early=True)[source]¶ Simple iterative gradientbased attack known as Basic Iterative Method, Projected Gradient Descent or FGSM^k.
Parameters:  inputs : numpy.ndarray
Batch of inputs with shape as expected by the underlying model.
 labels : numpy.ndarray
Class labels of the inputs as a vector of integers in [0, number of classes).
 unpack : bool
If true, returns the adversarial inputs as an array, otherwise returns Adversarial objects.
 binary_search : bool or int
Whether to perform a binary search over epsilon and stepsize, keeping their ratio constant and using their values to start the search. If False, hyperparameters are not optimized. Can also be an integer, specifying the number of binary search steps (default 20).
 epsilon : float
Limit on the perturbation size; if binary_search is True, this value is only for initialization and automatically adapted.
 stepsize : float
Step size for gradient descent; if binary_search is True, this value is only for initialization and automatically adapted.
 iterations : int
Number of iterations for each gradient descent run.
 random_start : bool
Start the attack from a random point rather than from the original input.
 return_early : bool
Whether an individual gradient descent run should stop as soon as an adversarial is found.


class
foolbox.attacks.
L2BasicIterativeAttack
(model=None, criterion=<foolbox.criteria.Misclassification object>, distance=<class 'foolbox.distances.MeanSquaredDistance'>, threshold=None)[source]¶ Modified version of the Basic Iterative Method that minimizes the L2 distance.
See also

as_generator
(self, a, binary_search=True, epsilon=0.3, stepsize=0.05, iterations=10, random_start=False, return_early=True)[source]¶ Simple iterative gradientbased attack known as Basic Iterative Method, Projected Gradient Descent or FGSM^k.
Parameters:  inputs : numpy.ndarray
Batch of inputs with shape as expected by the underlying model.
 labels : numpy.ndarray
Class labels of the inputs as a vector of integers in [0, number of classes).
 unpack : bool
If true, returns the adversarial inputs as an array, otherwise returns Adversarial objects.
 binary_search : bool or int
Whether to perform a binary search over epsilon and stepsize, keeping their ratio constant and using their values to start the search. If False, hyperparameters are not optimized. Can also be an integer, specifying the number of binary search steps (default 20).
 epsilon : float
Limit on the perturbation size; if binary_search is True, this value is only for initialization and automatically adapted.
 stepsize : float
Step size for gradient descent; if binary_search is True, this value is only for initialization and automatically adapted.
 iterations : int
Number of iterations for each gradient descent run.
 random_start : bool
Start the attack from a random point rather than from the original input.
 return_early : bool
Whether an individual gradient descent run should stop as soon as an adversarial is found.


class
foolbox.attacks.
ProjectedGradientDescentAttack
(model=None, criterion=<foolbox.criteria.Misclassification object>, distance=<class 'foolbox.distances.MeanSquaredDistance'>, threshold=None)[source]¶ The Projected Gradient Descent Attack introduced in [R367e8e10528a1] without random start.
When used without a random start, this attack is also known as Basic Iterative Method (BIM) or FGSM^k.
References
[R367e8e10528a1] Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu, “Towards Deep Learning Models Resistant to Adversarial Attacks”, https://arxiv.org/abs/1706.06083 
as_generator
(self, a, binary_search=True, epsilon=0.3, stepsize=0.01, iterations=40, random_start=False, return_early=True)[source]¶ Simple iterative gradientbased attack known as Basic Iterative Method, Projected Gradient Descent or FGSM^k.
Parameters:  inputs : numpy.ndarray
Batch of inputs with shape as expected by the underlying model.
 labels : numpy.ndarray
Class labels of the inputs as a vector of integers in [0, number of classes).
 unpack : bool
If true, returns the adversarial inputs as an array, otherwise returns Adversarial objects.
 binary_search : bool or int
Whether to perform a binary search over epsilon and stepsize, keeping their ratio constant and using their values to start the search. If False, hyperparameters are not optimized. Can also be an integer, specifying the number of binary search steps (default 20).
 epsilon : float
Limit on the perturbation size; if binary_search is True, this value is only for initialization and automatically adapted.
 stepsize : float
Step size for gradient descent; if binary_search is True, this value is only for initialization and automatically adapted.
 iterations : int
Number of iterations for each gradient descent run.
 random_start : bool
Start the attack from a random point rather than from the original input.
 return_early : bool
Whether an individual gradient descent run should stop as soon as an adversarial is found.


foolbox.attacks.
ProjectedGradientDescent
[source]¶ alias of
foolbox.attacks.iterative_projected_gradient.ProjectedGradientDescentAttack

foolbox.attacks.
PGD
[source]¶ alias of
foolbox.attacks.iterative_projected_gradient.ProjectedGradientDescentAttack

class
foolbox.attacks.
RandomStartProjectedGradientDescentAttack
(model=None, criterion=<foolbox.criteria.Misclassification object>, distance=<class 'foolbox.distances.MeanSquaredDistance'>, threshold=None)[source]¶ The Projected Gradient Descent Attack introduced in [Re6066bc39e141] with random start.
References
[Re6066bc39e141] Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu, “Towards Deep Learning Models Resistant to Adversarial Attacks”, https://arxiv.org/abs/1706.06083 See also

as_generator
(self, a, binary_search=True, epsilon=0.3, stepsize=0.01, iterations=40, random_start=True, return_early=True)[source]¶ Simple iterative gradientbased attack known as Basic Iterative Method, Projected Gradient Descent or FGSM^k.
Parameters:  inputs : numpy.ndarray
Batch of inputs with shape as expected by the underlying model.
 labels : numpy.ndarray
Class labels of the inputs as a vector of integers in [0, number of classes).
 unpack : bool
If true, returns the adversarial inputs as an array, otherwise returns Adversarial objects.
 binary_search : bool or int
Whether to perform a binary search over epsilon and stepsize, keeping their ratio constant and using their values to start the search. If False, hyperparameters are not optimized. Can also be an integer, specifying the number of binary search steps (default 20).
 epsilon : float
Limit on the perturbation size; if binary_search is True, this value is only for initialization and automatically adapted.
 stepsize : float
Step size for gradient descent; if binary_search is True, this value is only for initialization and automatically adapted.
 iterations : int
Number of iterations for each gradient descent run.
 random_start : bool
Start the attack from a random point rather than from the original input.
 return_early : bool
Whether an individual gradient descent run should stop as soon as an adversarial is found.


foolbox.attacks.
RandomProjectedGradientDescent
[source]¶ alias of
foolbox.attacks.iterative_projected_gradient.RandomStartProjectedGradientDescentAttack

foolbox.attacks.
RandomPGD
[source]¶ alias of
foolbox.attacks.iterative_projected_gradient.RandomStartProjectedGradientDescentAttack

class
foolbox.attacks.
AdamL1BasicIterativeAttack
(model=None, criterion=<foolbox.criteria.Misclassification object>, distance=<class 'foolbox.distances.MeanSquaredDistance'>, threshold=None)[source]¶ Modified version of the Basic Iterative Method that minimizes the L1 distance using the Adam optimizer.
See also

as_generator
(self, a, binary_search=True, epsilon=0.3, stepsize=0.05, iterations=10, random_start=False, return_early=True)[source]¶ Simple iterative gradientbased attack known as Basic Iterative Method, Projected Gradient Descent or FGSM^k.
Parameters:  input_or_adv : numpy.ndarray or
Adversarial
The original, unperturbed input as a numpy.ndarray or an
Adversarial
instance. label : int
The reference label of the original input. Must be passed if a is a numpy.ndarray, must not be passed if a is an
Adversarial
instance. unpack : bool
If true, returns the adversarial input, otherwise returns the Adversarial object.
 binary_search : bool or int
Whether to perform a binary search over epsilon and stepsize, keeping their ratio constant and using their values to start the search. If False, hyperparameters are not optimized. Can also be an integer, specifying the number of binary search steps (default 20).
 epsilon : float
Limit on the perturbation size; if binary_search is True, this value is only for initialization and automatically adapted.
 stepsize : float
Step size for gradient descent; if binary_search is True, this value is only for initialization and automatically adapted.
 iterations : int
Number of iterations for each gradient descent run.
 random_start : bool
Start the attack from a random point rather than from the original input.
 return_early : bool
Whether an individual gradient descent run should stop as soon as an adversarial is found.
 input_or_adv : numpy.ndarray or


class
foolbox.attacks.
AdamL2BasicIterativeAttack
(model=None, criterion=<foolbox.criteria.Misclassification object>, distance=<class 'foolbox.distances.MeanSquaredDistance'>, threshold=None)[source]¶ Modified version of the Basic Iterative Method that minimizes the L2 distance using the Adam optimizer.
See also

as_generator
(self, a, binary_search=True, epsilon=0.3, stepsize=0.05, iterations=10, random_start=False, return_early=True)[source]¶ Simple iterative gradientbased attack known as Basic Iterative Method, Projected Gradient Descent or FGSM^k.
Parameters:  input_or_adv : numpy.ndarray or
Adversarial
The original, unperturbed input as a numpy.ndarray or an
Adversarial
instance. label : int
The reference label of the original input. Must be passed if a is a numpy.ndarray, must not be passed if a is an
Adversarial
instance. unpack : bool
If true, returns the adversarial input, otherwise returns the Adversarial object.
 binary_search : bool or int
Whether to perform a binary search over epsilon and stepsize, keeping their ratio constant and using their values to start the search. If False, hyperparameters are not optimized. Can also be an integer, specifying the number of binary search steps (default 20).
 epsilon : float
Limit on the perturbation size; if binary_search is True, this value is only for initialization and automatically adapted.
 stepsize : float
Step size for gradient descent; if binary_search is True, this value is only for initialization and automatically adapted.
 iterations : int
Number of iterations for each gradient descent run.
 random_start : bool
Start the attack from a random point rather than from the original input.
 return_early : bool
Whether an individual gradient descent run should stop as soon as an adversarial is found.
 input_or_adv : numpy.ndarray or


class
foolbox.attacks.
AdamProjectedGradientDescentAttack
(model=None, criterion=<foolbox.criteria.Misclassification object>, distance=<class 'foolbox.distances.MeanSquaredDistance'>, threshold=None)[source]¶ The Projected Gradient Descent Attack introduced in [Re2d4f39a02051], [Re2d4f39a02052] without random start using the Adam optimizer.
When used without a random start, this attack is also known as Basic Iterative Method (BIM) or FGSM^k.
References
[Re2d4f39a02051] Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu, “Towards Deep Learning Models Resistant to Adversarial Attacks”, https://arxiv.org/abs/1706.06083 [Re2d4f39a02052] Nicholas Carlini, David Wagner: “Towards Evaluating the Robustness of Neural Networks”, https://arxiv.org/abs/1608.04644 
as_generator
(self, a, binary_search=True, epsilon=0.3, stepsize=0.01, iterations=40, random_start=False, return_early=True)[source]¶ Simple iterative gradientbased attack known as Basic Iterative Method, Projected Gradient Descent or FGSM^k.
Parameters:  input_or_adv : numpy.ndarray or
Adversarial
The original, unperturbed input as a numpy.ndarray or an
Adversarial
instance. label : int
The reference label of the original input. Must be passed if a is a numpy.ndarray, must not be passed if a is an
Adversarial
instance. unpack : bool
If true, returns the adversarial input, otherwise returns the Adversarial object.
 binary_search : bool or int
Whether to perform a binary search over epsilon and stepsize, keeping their ratio constant and using their values to start the search. If False, hyperparameters are not optimized. Can also be an integer, specifying the number of binary search steps (default 20).
 epsilon : float
Limit on the perturbation size; if binary_search is True, this value is only for initialization and automatically adapted.
 stepsize : float
Step size for gradient descent; if binary_search is True, this value is only for initialization and automatically adapted.
 iterations : int
Number of iterations for each gradient descent run.
 random_start : bool
Start the attack from a random point rather than from the original input.
 return_early : bool
Whether an individual gradient descent run should stop as soon as an adversarial is found.
 input_or_adv : numpy.ndarray or


foolbox.attacks.
AdamProjectedGradientDescent
[source]¶ alias of
foolbox.attacks.iterative_projected_gradient.AdamProjectedGradientDescentAttack

foolbox.attacks.
AdamPGD
[source]¶ alias of
foolbox.attacks.iterative_projected_gradient.AdamProjectedGradientDescentAttack

class
foolbox.attacks.
AdamRandomStartProjectedGradientDescentAttack
(model=None, criterion=<foolbox.criteria.Misclassification object>, distance=<class 'foolbox.distances.MeanSquaredDistance'>, threshold=None)[source]¶ The Projected Gradient Descent Attack introduced in [R3210aa3390851], [R3210aa3390852] with random start using the Adam optimizer.
References
[R3210aa3390851] Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu, “Towards Deep Learning Models Resistant to Adversarial Attacks”, https://arxiv.org/abs/1706.06083 [R3210aa3390852] Nicholas Carlini, David Wagner: “Towards Evaluating the Robustness of Neural Networks”, https://arxiv.org/abs/1608.04644 See also

as_generator
(self, a, binary_search=True, epsilon=0.3, stepsize=0.01, iterations=40, random_start=True, return_early=True)[source]¶ Simple iterative gradientbased attack known as Basic Iterative Method, Projected Gradient Descent or FGSM^k.
Parameters:  input_or_adv : numpy.ndarray or
Adversarial
The original, unperturbed input as a numpy.ndarray or an
Adversarial
instance. label : int
The reference label of the original input. Must be passed if a is a numpy.ndarray, must not be passed if a is an
Adversarial
instance. unpack : bool
If true, returns the adversarial input, otherwise returns the Adversarial object.
 binary_search : bool or int
Whether to perform a binary search over epsilon and stepsize, keeping their ratio constant and using their values to start the search. If False, hyperparameters are not optimized. Can also be an integer, specifying the number of binary search steps (default 20).
 epsilon : float
Limit on the perturbation size; if binary_search is True, this value is only for initialization and automatically adapted.
 stepsize : float
Step size for gradient descent; if binary_search is True, this value is only for initialization and automatically adapted.
 iterations : int
Number of iterations for each gradient descent run.
 random_start : bool
Start the attack from a random point rather than from the original input.
 return_early : bool
Whether an individual gradient descent run should stop as soon as an adversarial is found.
 input_or_adv : numpy.ndarray or


foolbox.attacks.
AdamRandomProjectedGradientDescent
[source]¶ alias of
foolbox.attacks.iterative_projected_gradient.AdamRandomStartProjectedGradientDescentAttack

foolbox.attacks.
AdamRandomPGD
[source]¶ alias of
foolbox.attacks.iterative_projected_gradient.AdamRandomStartProjectedGradientDescentAttack

class
foolbox.attacks.
MomentumIterativeAttack
(model=None, criterion=<foolbox.criteria.Misclassification object>, distance=<class 'foolbox.distances.MeanSquaredDistance'>, threshold=None)[source]¶ The Momentum Iterative Method attack introduced in [R86d363e1fb2f1]. It’s like the Basic Iterative Method or Projected Gradient Descent except that it uses momentum.
References
[R86d363e1fb2f1] Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, Jianguo Li, “Boosting Adversarial Attacks with Momentum”, https://arxiv.org/abs/1710.06081 
as_generator
(self, a, binary_search=True, epsilon=0.3, stepsize=0.06, iterations=10, decay_factor=1.0, random_start=False, return_early=True)[source]¶ Momentumbased iterative gradient attack known as Momentum Iterative Method.
Parameters:  inputs : numpy.ndarray
Batch of inputs with shape as expected by the underlying model.
 labels : numpy.ndarray
Class labels of the inputs as a vector of integers in [0, number of classes).
 unpack : bool
If true, returns the adversarial inputs as an array, otherwise returns Adversarial objects.
 binary_search : bool
Whether to perform a binary search over epsilon and stepsize, keeping their ratio constant and using their values to start the search. If False, hyperparameters are not optimized. Can also be an integer, specifying the number of binary search steps (default 20).
 epsilon : float
Limit on the perturbation size; if binary_search is True, this value is only for initialization and automatically adapted.
 stepsize : float
Step size for gradient descent; if binary_search is True, this value is only for initialization and automatically adapted.
 iterations : int
Number of iterations for each gradient descent run.
 decay_factor : float
Decay factor used by the momentum term.
 random_start : bool
Start the attack from a random point rather than from the original input.
 return_early : bool
Whether an individual gradient descent run should stop as soon as an adversarial is found.


foolbox.attacks.
MomentumIterativeMethod
[source]¶ alias of
foolbox.attacks.iterative_projected_gradient.MomentumIterativeAttack

class
foolbox.attacks.
DeepFoolAttack
(model=None, criterion=<foolbox.criteria.Misclassification object>, distance=<class 'foolbox.distances.MeanSquaredDistance'>, threshold=None)[source]¶ Simple and close to optimal gradientbased adversarial attack.
Implementes DeepFool introduced in [Rb4dd026407561].
References
[Rb4dd026407561] SeyedMohsen MoosaviDezfooli, Alhussein Fawzi, Pascal Frossard, “DeepFool: a simple and accurate method to fool deep neural networks”, https://arxiv.org/abs/1511.04599 
as_generator
(self, a, steps=100, subsample=10, p=None)[source]¶ Simple and close to optimal gradientbased adversarial attack.
Parameters:  input_or_adv : numpy.ndarray or
Adversarial
The original, unperturbed input as a numpy.ndarray or an
Adversarial
instance. label : int
The reference label of the original input. Must be passed if a is a numpy.ndarray, must not be passed if a is an
Adversarial
instance. unpack : bool
If true, returns the adversarial input, otherwise returns the Adversarial object.
 steps : int
Maximum number of steps to perform.
 subsample : int
Limit on the number of the most likely classes that should be considered. A small value is usually sufficient and much faster.
 p : int or float
Lpnorm that should be minimzed, must be 2 or np.inf.
 input_or_adv : numpy.ndarray or


class
foolbox.attacks.
NewtonFoolAttack
(model=None, criterion=<foolbox.criteria.Misclassification object>, distance=<class 'foolbox.distances.MeanSquaredDistance'>, threshold=None)[source]¶ Implements the NewtonFool Attack.
The attack was introduced in [R6a972939b3201].
References
[R6a972939b3201] Uyeong Jang et al., “Objective Metrics and Gradient Descent Algorithms for Adversarial Examples in Machine Learning”, https://dl.acm.org/citation.cfm?id=3134635 
as_generator
(self, a, max_iter=100, eta=0.01)[source]¶ Parameters:  input_or_adv : numpy.ndarray or
Adversarial
The original, unperturbed input as a numpy.ndarray or an
Adversarial
instance. label : int
The reference label of the original input. Must be passed if a is a numpy.ndarray, must not be passed if a is an
Adversarial
instance. unpack : bool
If true, returns the adversarial input, otherwise returns the Adversarial object.
 max_iter : int
The maximum number of iterations.
 eta : float
the eta coefficient
 input_or_adv : numpy.ndarray or


class
foolbox.attacks.
DeepFoolL2Attack
(model=None, criterion=<foolbox.criteria.Misclassification object>, distance=<class 'foolbox.distances.MeanSquaredDistance'>, threshold=None)[source]¶ 
as_generator
(self, a, steps=100, subsample=10)[source]¶ Simple and close to optimal gradientbased adversarial attack.
Parameters:  input_or_adv : numpy.ndarray or
Adversarial
The original, unperturbed input as a numpy.ndarray or an
Adversarial
instance. label : int
The reference label of the original input. Must be passed if a is a numpy.ndarray, must not be passed if a is an
Adversarial
instance. unpack : bool
If true, returns the adversarial input, otherwise returns the Adversarial object.
 steps : int
Maximum number of steps to perform.
 subsample : int
Limit on the number of the most likely classes that should be considered. A small value is usually sufficient and much faster.
 p : int or float
Lpnorm that should be minimzed, must be 2 or np.inf.
 input_or_adv : numpy.ndarray or


class
foolbox.attacks.
DeepFoolLinfinityAttack
(model=None, criterion=<foolbox.criteria.Misclassification object>, distance=<class 'foolbox.distances.MeanSquaredDistance'>, threshold=None)[source]¶ 
as_generator
(self, a, steps=100, subsample=10)[source]¶ Simple and close to optimal gradientbased adversarial attack.
Parameters:  input_or_adv : numpy.ndarray or
Adversarial
The original, unperturbed input as a numpy.ndarray or an
Adversarial
instance. label : int
The reference label of the original input. Must be passed if a is a numpy.ndarray, must not be passed if a is an
Adversarial
instance. unpack : bool
If true, returns the adversarial input, otherwise returns the Adversarial object.
 steps : int
Maximum number of steps to perform.
 subsample : int
Limit on the number of the most likely classes that should be considered. A small value is usually sufficient and much faster.
 p : int or float
Lpnorm that should be minimzed, must be 2 or np.inf.
 input_or_adv : numpy.ndarray or


class
foolbox.attacks.
ADefAttack
(model=None, criterion=<foolbox.criteria.Misclassification object>, distance=<class 'foolbox.distances.MeanSquaredDistance'>, threshold=None)[source]¶ Adversarial attack that distorts the image, i.e. changes the locations of pixels.
The algorithm is described in [Rf241e6d2664d1], a Repository with the original code can be found in [Rf241e6d2664d2].
References
[Rf241e6d2664d1] Rima Alaifari, Giovanni S. Alberti, and Tandri Gauksson: “ADef: an Iterative Algorithm to Construct Adversarial Deformations”, https://arxiv.org/abs/1804.07729 [Rf241e6d2664d2] https://gitlab.math.ethz.ch/tandrig/ADef/tree/master 
as_generator
(self, a, max_iter=100, smooth=1.0, subsample=10)[source]¶ Parameters:  input_or_adv : numpy.ndarray or
Adversarial
The original, unperturbed input as a numpy.ndarray or an
Adversarial
instance. label : int
The reference label of the original input. Must be passed if a is a numpy.ndarray, must not be passed if a is an
Adversarial
instance. unpack : bool
If true, returns the adversarial input, otherwise returns the Adversarial object.
 max_iter : int > 0
Maximum number of iterations (default max_iter = 100).
 smooth : float >= 0
Width of the Gaussian kernel used for smoothing. (default is smooth = 0 for no smoothing).
 subsample : int >= 2
Limit on the number of the most likely classes that should be considered. A small value is usually sufficient and much faster. (default subsample = 10)
 input_or_adv : numpy.ndarray or


class
foolbox.attacks.
SaliencyMapAttack
(model=None, criterion=<foolbox.criteria.Misclassification object>, distance=<class 'foolbox.distances.MeanSquaredDistance'>, threshold=None)[source]¶ Implements the Saliency Map Attack.
The attack was introduced in [R08e06ca693ba1].
References
[R08e06ca693ba1] Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, Ananthram Swami, “The Limitations of Deep Learning in Adversarial Settings”, https://arxiv.org/abs/1511.07528 
as_generator
(self, a, max_iter=2000, num_random_targets=0, fast=True, theta=0.1, max_perturbations_per_pixel=7)[source]¶ Implements the Saliency Map Attack.
Parameters:  input_or_adv : numpy.ndarray or
Adversarial
The original, unperturbed input as a numpy.ndarray or an
Adversarial
instance. label : int
The reference label of the original input. Must be passed if a is a numpy.ndarray, must not be passed if a is an
Adversarial
instance. max_iter : int
The maximum number of iterations to run.
 num_random_targets : int
Number of random target classes if no target class is given by the criterion.
 fast : bool
Whether to use the fast saliency map calculation.
 theta : float
perturbation per pixel relative to [min, max] range.
 max_perturbations_per_pixel : int
Maximum number of times a pixel can be modified.
 input_or_adv : numpy.ndarray or


class
foolbox.attacks.
IterativeGradientAttack
(model=None, criterion=<foolbox.criteria.Misclassification object>, distance=<class 'foolbox.distances.MeanSquaredDistance'>, threshold=None)[source]¶ Like GradientAttack but with several steps for each epsilon.

as_generator
(self, a, epsilons=100, max_epsilon=1, steps=10)[source]¶ Like GradientAttack but with several steps for each epsilon.
Parameters:  input_or_adv : numpy.ndarray or
Adversarial
The original, unperturbed input as a numpy.ndarray or an
Adversarial
instance. label : int
The reference label of the original input. Must be passed if a is a numpy.ndarray, must not be passed if a is an
Adversarial
instance. unpack : bool
If true, returns the adversarial input, otherwise returns the Adversarial object.
 epsilons : int or Iterable[float]
Either Iterable of step sizes in the gradient direction or number of step sizes between 0 and max_epsilon that should be tried.
 max_epsilon : float
Largest step size if epsilons is not an iterable.
 steps : int
Number of iterations to run.
 input_or_adv : numpy.ndarray or


class
foolbox.attacks.
IterativeGradientSignAttack
(model=None, criterion=<foolbox.criteria.Misclassification object>, distance=<class 'foolbox.distances.MeanSquaredDistance'>, threshold=None)[source]¶ Like GradientSignAttack but with several steps for each epsilon.

as_generator
(self, a, epsilons=100, max_epsilon=1, steps=10)[source]¶ Like GradientSignAttack but with several steps for each epsilon.
Parameters:  input_or_adv : numpy.ndarray or
Adversarial
The original, unperturbed input as a numpy.ndarray or an
Adversarial
instance. label : int
The reference label of the original input. Must be passed if a is a numpy.ndarray, must not be passed if a is an
Adversarial
instance. unpack : bool
If true, returns the adversarial input, otherwise returns the Adversarial object.
 epsilons : int or Iterable[float]
Either Iterable of step sizes in the direction of the sign of the gradient or number of step sizes between 0 and max_epsilon that should be tried.
 max_epsilon : float
Largest step size if epsilons is not an iterable.
 steps : int
Number of iterations to run.
 input_or_adv : numpy.ndarray or


class
foolbox.attacks.
CarliniWagnerL2Attack
(model=None, criterion=<foolbox.criteria.Misclassification object>, distance=<class 'foolbox.distances.MeanSquaredDistance'>, threshold=None)[source]¶ The L2 version of the Carlini & Wagner attack.
This attack is described in [Rc2cb572b91c51]. This implementation is based on the reference implementation by Carlini [Rc2cb572b91c52]. For bounds ≠ (0, 1), it differs from [Rc2cb572b91c52] because we normalize the squared L2 loss with the bounds.
References
[Rc2cb572b91c51] Nicholas Carlini, David Wagner: “Towards Evaluating the Robustness of Neural Networks”, https://arxiv.org/abs/1608.04644 [Rc2cb572b91c52] (1, 2) https://github.com/carlini/nn_robust_attacks 
as_generator
(self, a, binary_search_steps=5, max_iterations=1000, confidence=0, learning_rate=0.005, initial_const=0.01, abort_early=True)[source]¶ The L2 version of the Carlini & Wagner attack.
Parameters:  inputs : numpy.ndarray
Batch of inputs with shape as expected by the underlying model.
 labels : numpy.ndarray
Class labels of the inputs as a vector of integers in [0, number of classes).
 unpack : bool
If true, returns the adversarial inputs as an array, otherwise returns Adversarial objects.
 binary_search_steps : int
The number of steps for the binary search used to find the optimal tradeoffconstant between distance and confidence.
 max_iterations : int
The maximum number of iterations. Larger values are more accurate; setting it too small will require a large learning rate and will produce poor results.
 confidence : int or float
Confidence of adversarial examples: a higher value produces adversarials that are further away, but more strongly classified as adversarial.
 learning_rate : float
The learning rate for the attack algorithm. Smaller values produce better results but take longer to converge.
 initial_const : float
The initial tradeoffconstant to use to tune the relative importance of distance and confidence. If binary_search_steps is large, the initial constant is not important.
 abort_early : bool
If True, Adam will be aborted if the loss hasn’t decreased for some time (a tenth of max_iterations).


class
foolbox.attacks.
EADAttack
(model=None, criterion=<foolbox.criteria.Misclassification object>, distance=<class 'foolbox.distances.MeanSquaredDistance'>, threshold=None)[source]¶ Gradient based attack which uses an elasticnet regularization [1]. This implementation is based on the attacks description [1] and its reference implementation [2].
References
[Rf0e4124daa631] PinYu Chen (*), Yash Sharma (*), Huan Zhang, Jinfeng Yi, ChoJui Hsieh, “EAD: ElasticNet Attacks to Deep Neural Networks via Adversarial Examples”, https://arxiv.org/abs/1709.04114 [Rf0e4124daa632] PinYu Chen (*), Yash Sharma (*), Huan Zhang, Jinfeng Yi, ChoJui Hsieh, “Reference Implementation of ‘EAD: ElasticNet Attacks to Deep Neural Networks via Adversarial Examples’”, https://github.com/ysharma1126/EAD_Attack/blob/master/en_attack.py 
as_generator
(self, a, binary_search_steps=5, max_iterations=1000, confidence=0, initial_learning_rate=0.01, regularization=0.01, initial_const=0.01, abort_early=True)[source]¶ The L2 version of the Carlini & Wagner attack.
Parameters:  inputs : numpy.ndarray
Batch of inputs with shape as expected by the underlying model.
 labels : numpy.ndarray
Class labels of the inputs as a vector of integers in [0, number of classes).
 unpack : bool
If true, returns the adversarial inputs as an array, otherwise returns Adversarial objects.
 binary_search_steps : int
The number of steps for the binary search used to find the optimal tradeoffconstant between distance and confidence.
 max_iterations : int
The maximum number of iterations. Larger values are more accurate; setting it too small will require a large learning rate and will produce poor results.
 confidence : int or float
Confidence of adversarial examples: a higher value produces adversarials that are further away, but more strongly classified as adversarial.
 initial_learning_rate : float
The initial learning rate for the attack algorithm. Smaller values produce better results but take longer to converge. During the attack a squareroot decay in the learning rate is performed.
 initial_const : float
The initial tradeoffconstant to use to tune the relative importance of distance and confidence. If binary_search_steps is large, the initial constant is not important.
 regularization : float
The L1 regularization parameter (also called beta). A value of 0 corresponds to the
attacks.CarliniWagnerL2Attack
attack. abort_early : bool
If True, Adam will be aborted if the loss hasn’t decreased for some time (a tenth of max_iterations).

static
best_other_class
(logits, exclude)[source]¶ Returns the index of the largest logit, ignoring the class that is passed as exclude.


class
foolbox.attacks.
DecoupledDirectionNormL2Attack
(model=None, criterion=<foolbox.criteria.Misclassification object>, distance=<class 'foolbox.distances.MeanSquaredDistance'>, threshold=None)[source]¶ The Decoupled Direction and Norm L2 adversarial attack from [R0e9d4da0ab481].
References
[R0e9d4da0ab481] Jérôme Rony, Luiz G. Hafemann, Luiz S. Oliveira, Ismail Ben Ayed, Robert Sabourin, Eric Granger, “Decoupling Direction and Norm for Efficient GradientBased L2 Adversarial Attacks and Defenses”, https://arxiv.org/abs/1811.09600 
as_generator
(self, a, steps=100, gamma=0.05, initial_norm=1, quantize=True, levels=256)[source]¶ The Decoupled Direction and Norm L2 adversarial attack.
Parameters:  input_or_adv : numpy.ndarray or
Adversarial
The original, unperturbed input as a numpy.ndarray or an
Adversarial
instance. label : int
The reference label of the original input. Must be passed if a is a numpy.ndarray, must not be passed if a is an
Adversarial
instance. unpack : bool
If true, returns the adversarial input, otherwise returns the Adversarial object.
 steps : int
Number of steps for the optimization.
 gamma : float, optional
Factor by which the norm will be modified. new_norm = norm * (1 + or  gamma).
 init_norm : float, optional
Initial value for the norm.
 quantize : bool, optional
If True, the returned adversarials will have quantized values to the specified number of levels.
 levels : int, optional
Number of levels to use for quantization (e.g. 256 for 8 bit images).
 input_or_adv : numpy.ndarray or


class
foolbox.attacks.
SparseL1BasicIterativeAttack
(model=None, criterion=<foolbox.criteria.Misclassification object>, distance=<class 'foolbox.distances.MeanSquaredDistance'>, threshold=None)[source]¶ Sparse version of the Basic Iterative Method that minimizes the L1 distance introduced in [R0591d14da1c31].
References
[R0591d14da1c31] Florian Tramèr, Dan Boneh, “Adversarial Training and Robustness for Multiple Perturbations”, https://arxiv.org/abs/1904.13000 See also

as_generator
(self, a, q=80.0, binary_search=True, epsilon=0.3, stepsize=0.05, iterations=10, random_start=False, return_early=True)[source]¶ Sparse version of a gradientbased attack that minimizes the L1 distance.
Parameters:  inputs : numpy.ndarray
Batch of inputs with shape as expected by the underlying model.
 labels : numpy.ndarray
Class labels of the inputs as a vector of integers in [0, number of classes).
 unpack : bool
If true, returns the adversarial inputs as an array, otherwise returns Adversarial objects.
 q : float
Relative percentile to make gradients sparse (must be in [0, 100))
 binary_search : bool or int
Whether to perform a binary search over epsilon and stepsize, keeping their ratio constant and using their values to start the search. If False, hyperparameters are not optimized. Can also be an integer, specifying the number of binary search steps (default 20).
 epsilon : float
Limit on the perturbation size; if binary_search is True, this value is only for initialization and automatically adapted.
 stepsize : float
Step size for gradient descent; if binary_search is True, this value is only for initialization and automatically adapted.
 iterations : int
Number of iterations for each gradient descent run.
 random_start : bool
Start the attack from a random point rather than from the original input.
 return_early : bool
Whether an individual gradient descent run should stop as soon as an adversarial is found.


class
foolbox.attacks.
VirtualAdversarialAttack
(model=None, criterion=<foolbox.criteria.Misclassification object>, distance=<class 'foolbox.distances.MeanSquaredDistance'>, threshold=None)[source]¶ Calculate an untargeted adversarial perturbation by performing a approximated second order optimization step on the KL divergence between the unperturbed predictions and the predictions for the adversarial perturbation. This attack was introduced in [Rc6516d158ac21].
References
[Rc6516d158ac21] Takeru Miyato, Shinichi Maeda, Masanori Koyama, Ken Nakae, Shin Ishii, “Distributional Smoothing with Virtual Adversarial Training”, https://arxiv.org/abs/1507.00677 
as_generator
(self, a, xi=1e05, iterations=1, epsilons=1000, max_epsilon=0.3)[source]¶ Parameters:  inputs : numpy.ndarray
Batch of inputs with shape as expected by the underlying model.
 labels : numpy.ndarray
Class labels of the inputs as a vector of integers in [0, number of classes).
 unpack : bool
If true, returns the adversarial inputs as an array, otherwise returns Adversarial objects.
 xi : float
The finite difference size for performing the power method.
 iterations : int
Number of iterations to perform power method to search for second order perturbation of KL divergence.
 epsilons : int or Iterable[float]
Either Iterable of step sizes in the direction of the sign of the gradient or number of step sizes between 0 and max_epsilon that should be tried.
 max_epsilon : float
Largest step size if epsilons is not an iterable.
