ART 1.5.2
This release of ART 1.5.2 provides updates to ART 1.5.
Added
- Added new method
reset_patchtoart.attacks.evasion.adversarial_patch.*to reset patch (#863) - Added passing
kwargsto internal attacks ofart.attacks.evasion.AutoAttack(#850) - Added
art.estimators.classification.BlackBoxClassifierNeuralNetworkas black-box classifier for neural network models (#849) - Added support for
channels_first=Falseforart.attacks.evasion.ShadowAttackin PyTorch (#848)
Changed
- Changed Numpy requirements to be less strict to resolve conflicts in dependencies (#879)
- Changed estimator requirements for
art.attacks.evasion.SquareAttackandart.attacks.evasion.SimBAto includeNeuralNetworkMixinrequiring neural network models (#849)
Removed
[None]
Fixed
- Fixed
BaseEstimator.set_paramsto setpreprocessingandpreprocessing_defencescorrectly 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.StandardisationMeanStdPyTorchto maintain correct dtype (#890) - Fixed type conversion in
art.evaluations.security_curve.SecurityCurveto be explicit (#886) - Fixed dtype in
art.attacks.evasion.SquareAttackfornorm=2to maintain correct type (#877) - Fixed missing
CarliniWagnerASRinart.attacks.evasionnamespace (#873) - Fixed support for CUDA i `art.estimators.classification.PyTorchClassifier.loss (#862)
- Fixed bug in
art.attacks.evasion.AutoProjectedGradientDescentfor 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.ProjectedGradientDescentPyTorchandart.attacks.evasion.ProjectedGradientDescentTensorFlowV2to use correct labels (#833)