Trusted-AI/adversarial-robustness-toolbox 0.8.0

ART 0.8.0

on GitHub

This release includes new evasion attacks, like ZOO, boundary attack and the adversarial patch, as well as the capacity to break non-differentiable defences.

Added

• ZOO black-box attack (class ZooAttack)
• Decision boundary black-box attack (class BoundaryAttack)
• Adversarial patch (class AdversarialPatch)
• Function to estimate gradients in Preprocessor API, along with its implementation for all concrete instances.
  This allows to break non-differentiable defences.
• Attributes apply_fit and apply_predict in Preprocessor API that indicate if a defence should be used at training and/or test time
• Classifiers are now capable of running a full backward pass through defences
• save function for TensorFlow models
• New notebook with usage example for the adversarial patch
• New notebook showing how to synthesize an adversarially robust architecture (see ICLR SafeML Workshop 2019: Evolutionary Search for Adversarially Robust Neural Network by M. Sinn, M. Wistuba, B. Buesser, M.-I. Nicolae, M.N. Tran)

Changed

• [Breaking change] Defences in classifiers are now to be specified as Preprocessor instances instead of strings
• [Breaking change] Parameter random_init in FastGradientMethod, ProjectedGradientDescent and BasicIterativeMethod has been renamed to num_random_init and allows now to specify the number of random initialization to run before choosing the best attack
• Possibility to specify batch size when calling get_activations from Classifier API