ART 1.5.0

Added

• Added a new module art.evaluations for evaluation tools that go beyond creating adversarial examples and create insights into the robustness of machine learning models beyond adversarial accuracy and build on art.estimators and art.attacks as much as possible. The first implemented evaluation tool is art.evaluations.SecurityCurve which calculates the security curve, a popular tool to evaluate robustness against evasion, using art.attacks.evasion.ProjectedGradientDescent and provides evaluation of potential gradient masking in the evaluated model. (#654)

• Added support for perturbation masks in art.attacks.evasion.AutoProjectedGradientDescent similar as in art.attacks.evasion.ProjectedGradientDescent and added Boolean masks for patch location sampling in Dpatch and all AdversarialPatch attacks to enable pixel masks defining regions where patch locations are sampled from during patch training or where trained patches can be applied.

• Added preprocessing for Infinite (IIR) and Finite Impulse Response (FIR) filtering for Room Acoustics Modelling in framework-agnostic (art.preprocessing.audio.LFilter) and PyTorch-specific (art.preprocessing.audio.LFilterPyTorch) implementations as the first tool for physical environment simulation for audio data in art.preprocessing.audio. Additional tools will be added in future releases. (#744)

• Added Expectation over Transformation (EoT) to art.preprocessing.expectation_over_transformation with a first implementation of sampling image rotation for classification tasks framework-specific for TensorFlow v2 (art.preprocessing.expectation_over_transformation.EOTImageRotationTensorFlowV2) providing full support for gradient backpropagation through EoT. Additional EoTs will be added in future releases. (#744)

• Added support for multi-modal inputs in ProjectedGradientDescent attacks and FastGradientMethod attack with broadcastable arguments eps and eps_step as np.ndarray to enable attacks against, for example, images with multi-modal color channels. (#691)

• Added Database Reconstruction attack in the new module art.attacks.inference.reconstruction.DatabaseReconstruction enabling evaluation of the privacy of machine learning models by reconstructing one removed sample of the training dataset. The attack is demonstrated in a new notebook on models trained non-privately and with differential privacy using the Differential Privacy Library (DiffPrivLib) as defense. (#759)

• Added support for one-hot encoded feature definition in black-box attribute inference attacks. (#768)

• Added a new model-specific speech recognition estimator for Lingvo ASR in art.estimators.speech_recognition.TensorFlowLingvoASR. (#584)

• Added a framework-independent implementation of the Imperceptible ASR attack with loss support for TensorFlow and PyTorch in art.attacks.evasion.ImperceptibleASR. (#719, #760)

• Added Clean Label Backdoor poisoning attack in art.attacks.poisoning.PoisoningAttackCleanLabelBackdoor. (#725)

• Added Strong Intentional Perturbation (STRIP) defense against poisoning attacks in art.defences..transformer.poisoning.STRIP. (#656)

• Added Label-only Boundary Distance Attack art.attacks.inference.membership_inference.LabelOnlyDecisionBoundary and Label-only Gap Attack art.attacks.inference.membership_inference.LabelOnlyGapAttack for membership inference attacks on classification estimators. (#720)

• Added support for preprocessing and preprocessing defences in the PyTorch-specific implementation of the Imperceptible ASR attack in art.attacks.evasion.ImperceptibleASRPyTorch. (#763)

• Added a robust version of evasion attack DPatch in art.attacks.evasion.RobustDPatch against object detectors by adding improvements like expectation over transformation steps, fixed patch location, etc. (#751)

• Added optional support for Automatic Mixed Precision (AMP) in art.estimators.classification.PyTochClassifier to facilitate mix-precision computations and increase performance. (#619)

• Added the Brendel & Bethge evasion attack in art.attacks.evasion.BrendelBethgeAttack based on the original reference implementation. (#626)

• Added framework-agnostic support for Randomized Smoothing estimators in addition to framework-specific implementations in TensorFlow v2 and PyTorch. (#738)

• Added an optional progress bar to art.utils.get_file to facilitate downloading large files. (#698)

• Added support for perturbation masks in HopSkipJump evasion attack in art.attacks.evasion.HopSkipJump. (#653)

Changed

• Changed preprocessing defenses and input standardisation with mean and standard deviation by combining all preprocessing into a single preprocessing API defined in the new module art.preprocessing. Existing preprocessing defenses remain in art.defences.preprocessor, but are treated as equal and run with the same API and code as general preprocessing tools in art.preprocessing. The standardisation is now a preprocessing tool that is implemented framework-specific for PyTorch and TensorFlow v2 in forward and backward direction. Estimators for art.estimators.classification and art.estimators.object_detection in TensorFlow v2 and PyTorch set up with all framework-specific preprocessing steps will prepend the preprocessing directly to the model to evaluate output and backpropagate gradients in a single step through the model and (chained) preprocessing instead of previously two separate steps for improved performance. Framework independent preprocessing tools will continue to be evaluated in a step separate from the model. This change also enable full support for any model-specific standardisation/normalisation functions for the model inputs and their gradients. (#629)

• Changed Preprocessor and Postprocessor APIs to simplify them by defining reused methods and the most common property values as defaults in the API. The default for art.defences.preprocessor.preprocessor.Preprocessor.estimate_gradient in framework-agnostic preprocessing is Backward Pass Differentiable Approximation (BPDA) with identity function, which can be customized with accurate or better approximations by implementing estimate_gradient. (#752)

• Changed random restarts in all ProjectedGradientDescent implementations to collect the successful adversarial examples of each random restart instead of previously only keeping the adversarial examples of the most successful random restart. Adversarial examples of previous random restart iterations are overwritten by adversarial examples of later random restart iterations. This leads to equal or better adversarial accuracies compared to previous releases and changes the order of processing the input samples to first complete all random restarts of a batch before processing the next batch instead of looping over all batches in each random restart. (#765)

• Changed order of mask application and normalization of the perturbation in all ProjectedGradientDescent and FastGradientMethod attacks to now first apply the mask to the loss_gradients and subsequently normalize only the remaining, un-masked perturbation. That way the resulting perturbation can directly be compared to the attack budget eps. (#711)

• Changed location of implementation and default values of properties channels_first, clip_values, and input_shape in art.estimators to facilitate the creation of customs estimators not present in art.estimators.

• Changed Spectral Signature Defense by removing argument num_classes and replacing it with the estimator’s nb_classes property and renaming parameter ub_pct_poison to expected_pp_poison. (#678)

• Changed the ART directory path for datasets and model data stored in ART_DATA_PATH to be configurable after importing ART. (#701)

• Changed preprocessing defence art.defences.preprocessor.JpegCompression to support any number of channels in addition to the already supported inputs with 1 and 3 channels. (#700)

• Changed calculation of perturbation and direction in art.attacks.evasion.BoundaryAttack to follow the reference implementation. These changes result in faster convergence and smaller perturbations. (#761)

Removed

[None]

Fixed

• Fixed bug in definition and application of norm p in cost matrix in Wasserstein evasion attack art.attacks.evasion.Wasserstein present in the reference implementation. (#712)

• Fixed handling of fractional batches in Zeroth Order Optimization (ZOO) attack in art.attacks.evasion.ZOOAttack to prevent errors caused by shape mismatches for batches smaller than batch_size. (#755)