This release of ART 1.3.2 provides updates to ART 1.3.1.

Added

Added verbose parameter for CarliniL2Method, CarliniLInfMethod, and DeepFool attacks to disable progress bars.

Changed

Changed the Wasserstein attack to support rectangular images as input (#527)
Changed UniversalPerturbation attack to use true labels if provided in internal attacks (#526)
Allow None as input for parameter `preprocessing of estimators (#493)
Allow eps to be larger than eps_step in ProjectedGradientDescent attacks if norm is not np.inf (#495)

[None]

Fixed import path for ProjectedGradientDescend option in UniversalPerturbation attack (#525)
Fixed support for arrays as clip_values in ProjectedGradientDescentPyTorch attack for PyTorch (#521)
Fixed success criteria for targeted attacks with AutoProjectedGradientDescend (#513)
Fixed success criteria for attacks used in AutoAttack (#508)
Fixed example for Fast-is-better-than-Free adversarial training (#506)
Fixed dtype in AutoProjectedGradientDescent and SquareAttack for testing output type of estimator (#499)
Fixed parameters in _augment_images_with_patch calls of attack DPatch (#493)