ART 1.5.2
This release of ART 1.5.2 provides updates to ART 1.5.
Added
- Added new method
reset_patch
toart.attacks.evasion.adversarial_patch.*
to reset patch (#863) - Added passing
kwargs
to internal attacks ofart.attacks.evasion.AutoAttack
(#850) - Added
art.estimators.classification.BlackBoxClassifierNeuralNetwork
as black-box classifier for neural network models (#849) - Added support for
channels_first=False
forart.attacks.evasion.ShadowAttack
in PyTorch (#848)
Changed
- Changed Numpy requirements to be less strict to resolve conflicts in dependencies (#879)
- Changed estimator requirements for
art.attacks.evasion.SquareAttack
andart.attacks.evasion.SimBA
to includeNeuralNetworkMixin
requiring neural network models (#849)
Removed
[None]
Fixed
- Fixed
BaseEstimator.set_params
to setpreprocessing
andpreprocessing_defences
correctly by accounting forart.preprocessing.standardisation_mean_std
(#901) - Fixed support for CUDA in
art.attacks.inference.membership_inference.MembershipInferenceBlackBox.infer
(#899) - Fixed return in
art.preprocessing.standardisation_mean_std.StandardisationMeanStdPyTorch
to maintain correct dtype (#890) - Fixed type conversion in
art.evaluations.security_curve.SecurityCurve
to be explicit (#886) - Fixed dtype in
art.attacks.evasion.SquareAttack
fornorm=2
to maintain correct type (#877) - Fixed missing
CarliniWagnerASR
inart.attacks.evasion
namespace (#873) - Fixed support for CUDA i `art.estimators.classification.PyTorchClassifier.loss (#862)
- Fixed bug in
art.attacks.evasion.AutoProjectedGradientDescent
for targeted attack to correctly detect successful iteration steps and added robust stopping criteria if loss becomes zero (#860) - Fixed bug in initialisation of search space in
art.attacks.evasion.SaliencyMapMethod
(#843) - Fixed bug in support for video data in
art.attacks.evasion.adversarial_patch.AdversarialPatchNumpy
(#838) - Fixed bug in logged success rate of
art.attacks.evasion.ProjectedGradientDescentPyTorch
andart.attacks.evasion.ProjectedGradientDescentTensorFlowV2
to use correct labels (#833)