This release of ART 1.3.3 provides updates to ART 1.3.

Added

Added support for rectangular images and videos (with square and rectangular frames) to the attacks in art.attacks.evasion.adversarial_patch.AdversarialPatch. The framework-independent implementation AdversarialPatchNumpy supports videos of shape NFCHW or NFHWC and the framework-specific implementation for TensorFlow v2 AdversarialPatchTensorFlowV2 supports videos of shape NFHWC. For video data the same patch will be located at the same position on all frames. (#567)
Added a warning to ShadowAttack to inform users that this implementation currently only works on a single sample in a batch size of one. (#556)

Changed

The Dockerfile will now automatically check if requirements.txt contains newer versions of the dependencies.
Changed the CLEVER metric art.metric.clever_t to only calculate required class gradients which results in a speed up of a factor of ~4. (#539)
Changed the metric art.metrics.wasserstein_distance to automatically flatten the weights of the two inputs. (#545)
Changed art.attacks.evasion.SquareAttack to use model predictions if true labels are not provided to method generate to follow the convention of the other attacks in ART. (#537)

[None]

Fixed method set_params in art.attacks.evasion.projected_gradient_descent.ProjectedGradientDescent to correctly update the attributes of the parent class. The attributes of the actual attack implementation have been set correctly before this fix. (#560)