Other attacks

class foolbox.attacks.BinarizationRefinementAttack(model=None, criterion=<foolbox.criteria.Misclassification object>, distance=<class 'foolbox.distances.MeanSquaredDistance'>, threshold=None)

For models that preprocess their inputs by binarizing the inputs, this attack can improve adversarials found by other attacks. It does os by utilizing information about the binarization and mapping values to the corresponding value in the clean input or to the right side of the threshold.

as_generator(self, a, starting_point=None, threshold=None, included_in='upper')

For models that preprocess their inputs by binarizing the inputs, this attack can improve adversarials found by other attacks. It does this by utilizing information about the binarization and mapping values to the corresponding value in the clean input or to the right side of the threshold.

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.

starting_point : numpy.ndarray

Adversarial input to use as a starting point.

threshold : float

The treshold used by the models binarization. If none, defaults to (model.bounds()[1] - model.bounds()[0]) / 2.

included_in : str

Whether the threshold value itself belongs to the lower or upper interval.

class foolbox.attacks.PrecomputedAdversarialsAttack(model=None, criterion=<foolbox.criteria.Misclassification object>, distance=<class 'foolbox.distances.MeanSquaredDistance'>, threshold=None)

Attacks a model using precomputed adversarial candidates.

as_generator(self, a, candidate_inputs, candidate_outputs)

Attacks a model using precomputed adversarial candidates.

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.

candidate_inputs : numpy.ndarray

The original inputs that will be expected by this attack.

candidate_outputs : numpy.ndarray

The adversarial candidates corresponding to the inputs.

class foolbox.attacks.InversionAttack(model=None, criterion=<foolbox.criteria.Misclassification object>, distance=<class 'foolbox.distances.MeanSquaredDistance'>, threshold=None)

Creates “negative images” by inverting the pixel values according to [R57cf8375f1ff-1].

References

[R57cf8375f1ff-1]

Hossein Hosseini, Baicen Xiao, Mayoore Jaiswal, Radha Poovendran, “On the Limitation of Convolutional Neural Networks in Recognizing Negative Images”, https://arxiv.org/abs/1607.02533

as_generator(self, a)

Creates “negative images” by inverting the pixel values.

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.