ART 1.8.0

This release of ART v1.8.0 introduces the first estimators for object tracking and regression, adds a general model-independent object detection estimator and new membership inference attacks.

Added

• Added estimator for object tracker GOTURN in PyTorch in art.estimators.object_tracking.PyTorchGoturn (#1318)
• Added estimator for scikit-learn DecisionTreeRegressor in art.estimators.regression.ScikitlearnDecistionTreeRegressor and added compatibility in attacks AttributeInferenceBlackBox and MembershipInferenceBlackBox (#1272)
• Added general estimator for all object detection models of torchvision in art.estimators.object_detection.PyTorchObjectDetector (#1295)
• Added membership inference attack based on boundary attacks with general threshold selection by Li and Zhang (#1197)

Changed

• Changed art.estimators.classification.BlackboxClassifier* to also accept recorded input/prediction data pairs, instead of a callable providing predictions by evaluating the attacked model, enabling attacks on prediction data only without the necessity for direct access to the attacked model (#1247)
• Moved patched Lingvo decoder to art.contrib (#1261)

Removed

• Removed art.classifiers and art.wappers, both modules have been replaced with tools in art.preprocessing.expectation_over_transformation, art.estimators.classification and art.estimators.classification.QueryEfficientGradientEstimationClassifier (#1256)

Fixed

[None]

ART 1.7.2

This release of ART 1.7.2 provides updates to ART 1.7.

Added

[None]

Changed

[None]

Removed

[None]

Fixed

• Fixed missing support for index labels in PyTorchClassifier.compute_loss. (#1264)
• Fixed missing support for float in argument min_epsilon of BoundaryAttack. (#1262)
• Fixed support for channels first images in art/attacks/poisoning/perturbations/image_perturbations.insert_image. (#1290)

ART 1.7.1

This release of ART 1.7.1 provides updates to ART 1.7.

Added

• Added wrapper Mp3CompressionPyTorch for Mp3Compression to make it compatible with PyTorch-specific attack implementations. (#1210)
• Added new install option non-framework to setup.py to install all non-framework dependencies of ART. (#1209)
• Added wrapper VideoCompressionPyTorch for VideoCompression to make it compatible with PyTorch-specific attack implementations. (#1210)

Changed

• Changed Mp3Compression to add back reapplication of normalization to the compressed result. (#1210)
• Changed KerasClassifier.fit to use batching provided by the method fit of the Keras model. (#1182)

Removed

[None]

Fixed

• Fixed bug of not passing user-provided device type, and instead always using default gpu, to standardisation preprocessor in all PyTorchEstimator by using user-provided device type. (#1223)
• Fixed bug in method BaseEstimator.fit_generator for fitting generators in cases where preprocessing is defined to not apply preprocessing twice. (#1219)
• Fixed bug in ImperceptibleASRPyTorch to prevent NaN loss value for batch size larger than 1 by removing unnecessary zero-padding. (#1198)
• Fixed two bugs in OverTheAirFlickeringPyTorch by making sure that the regularization norms are computed over the whole batch of perturbations, rather than per sample's perturbation and second that the "roll" operations are performed over the batch samples, rather than over the frames. (#1192)
• Fixed bug in SpectralSignatureDefense, that lead to rejections of all clean images, by correctly indexing the label data. (#1189)
• Fixed bug of accidentally removed checks for apply_fit and apply_predict properties of framework-independent Preprocessor tools in PyTorchEstimator and TensorFlowV2Estimator. With the bug the Preprocessor tools were always applied in methods fit and predict independent of the values of apply_fit and apply_predict. (#1181)
• Fixed bug in MembershipInferenceBlackBoxRemove.infer by removing unnecessary shuffling of the test data. (#1173)
• Fixed bug in PixelAttack and ThresholdAttack by casting input data to correct dtype. (#1175)

ART 1.7.0

This release of ART v1.7.0 introduces many new evasion and inference attacks providing support for the evaluation of malware or tabular data classification, new query-efficient black-box (GeoDA) and strong white-box (Feature Adversaries) evaluation methods. Furthermore, this release introduces an easy to use estimator for Espresso ASR models to facilitate ASR research and connect Espresso and ART. This release also introduces support for binary classification with single outputs in neural networks classifiers and selected attacks. Many more new features and details can be found below:

Added

• Added LowProFool evasion attack for imperceptible attacks on tabular data classification in art.attacks.evasion.LowProFool. (#1063)
• Added Over-the-Air-Flickering attack in PyTorch for evasion on video classifiers in art.attacks.evasion.OverTheAirFlickeringPyTorch. (#1077, #1102)
• Added API for speech recognition estimators compatible with Imperceptible ASR attack in PyTorch. (#1052)
• Added Carlini&Wagner evasion attack with perturbations in L0-norm in art.attacks.evasion.CarliniL0Method. (#844, #1109)
• Added support for Deep Speech v3 in PyTorchDeepSpeech estimator. (#1107)
• Added support for TensorBoard collecting evolution of norms (L1, L2, and Linf) of loss gradients per batch, adversarial patch, and total loss and its model-specific components where available (e.g. PyTochFasterRCNN) in AdversarialPatchPyTorch, AdversarialPatchTensorFlow, FastGradientMethod, and all ProjectedGradientDescent* attacks. (#1071)
• Added MalwareGDTensorFlow attack for evasion on malware classification of portable executables supporting append based, section insertion, slack manipulation, and DOS header attacks. (#1015)
• Added Geometric Decision-based Attack (GeoDA) in art.attacks.evasion.GeoDA for query-efficient black-box attacks on decision labels using DCT noise. (#1001)
• Added Feature Adversaries framework-specific in PyTorch and TensorFlow v2 as efficient white-box attack generating adversarial examples imitating intermediate representations at multiple layers in art.attacks.evasion.FeatureAdversaries*. (#1128, #1142, #1156)
• Added attribute inference attack based on membership inference in art.attacks.inference.AttributeInferenceMembership. (#1132)
• Added support for binary classification with neural networks with a single output neuron in FastGradientMethod, and all ProjectedGradientDescent* attacks. Neural network binary classifiers with a single output require setting nb_classes=2 and labels y in shape (nb_samples, 1) or (nb_samples,) containing 0 or 1. Backward compatibility for binary classifiers with two outputs is guaranteed with nb_classes=2 and labels y one-hot-encoded in shape (nb_samples, 2). (#1118)
• Added estimator for Espresso ASR models in art.estimators.speech_recognition.PyTorchEspresso with support for attacks with FastGradientMethod, ProjectedGradientDescent and ImperceptibleASRPyTorch. (#1036)
• Added deprecation warnings for art.classifiers and art.wrappers to be replace with art.estimators. (#1154)

Changed

• Changed art.utils.load_iris to use Iris dataset from sklearn.datasets instead of archive.ics.uci.edu. (#1097 )
• Changed HopSkipJump to check for NaN in the adversarial example candidates and return original (benign) sample if at least one NaN is detected. (#1124)
• Changed SquareAttack to accept user-defined loss and adversarial criterium definitions to enable black-box attacks on all machine learning tasks on images beyond classification. (#1127)
• Changed PyTorchFasterRCNN.loss_gradients to process each sample separately to avoid issues with gradient propagation with torch>=1.7. (#1138)

Removed

[None]

Fixed

• Fixed workaround in art.defences.preprocessor.Mp3Compression related to a bug in earlier versions of pydub. (#419)
• Fixed bug in Pixel Attack and Threshold Attack for images with pixels in range [0, 1]. (#990)

ART 1.6.2

This release of ART 1.6.2 provides updates to ART 1.6.

Added

• Added targeted option to RobustDpatch (#1069)
• Added option standardise_output to define provided label format (#1069)
• Added property native_label_is_pytorch_format to object detectors to define label format expected by the model (#1069)

Changed

• Changed Dpatch and RobustDpatch to work internally with PyTorchFasterRCNN's object detection label format and convert labels if provided in TensorFlowFasterRCNN's format accordingly using option standardise_output (#1069)
• Change setup.py to only contain core dependencies in install_requires and added additional install options tensorflow_image, tensorflow_audio, pytorch_image, and pytorch_audio (#1116)
• Changed check for version of torch and torchvision in AdversarialPatchPyTorch to account for suffixes like +cu102 (#1115)
• Changed art.utils.load_iris to use sklearn.datasets.load_iris instead of download from https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data (#1097)

Removed

• Removed unnecessary requirement for scores in labels y for TensorFlowFasterRCNN.loss_gradient and PyTorchFasterRCNN.loss_gradient (#1069)

Fixed

• Fixed docstrings of methods predict and loss_gradient to correctly describe the expected and provided label format (#1069)
• Fixed bug of missing transfer of tensor to device ProjectedGradientDescentPyTorch (#1076)
• Fixed bug resulting in wrong loss gradients calculated with ScikitlearnLogisticRegression.loss_gradient (#1065)

ART 1.6.1

This release of ART 1.6.1 provides updates to ART 1.6.

Added

• Added a notebook showing an example of Expectation over Transformation (EoT) sampling with ART to generate adversarial examples that are robust against rotation in image classification tasks. (#1051)
• Added a check for valid combinations of stride, freq_dim and image size in SimBA attack. (#1037)
• Added accurate gradient estimation to LFilter audio preprocessing. (#1002)
• Added support for multiple layers to be targeted by BullseyePolytopeAttackPyTorch attack to increase effectiveness in end-to-end scenarios. (#1003)
• Added check and ValueError to provide explanation for too large nb_parallel values in ZooAttack. (#988)

Changed

• Changed TensorFlowV2Classifier.get_activations to accept negative layer indexes. (#1054)
• Tested BoundaryAttack and HopSkipJump attacks with batch_size larger than 1 and changed default value to batch_size=64. (#971)

Removed

[None]

Fixed

• Fixed bug in Dpatch attack which did not update the patch, being optimised, onto the images used for loss gradient calculation leading to iterations with the constant, initially, applied patches. (#1049)
• Fixed bug in BullseyePolytopeAttackPyTorch attack where attacking multiple layers of the underlying model only perturbed the first of all input images. (#1046)
• Fixed return value of TensorFlowV2Classifier.get_activations to a list of strings. (#1011)
• Fixed bug in TensorFlowV2Classifier.loss_gradient by adding labels to application of preprocessing step to enable EoT preprocessing steps that increase the number of samples and labels. This change does not affect the accuracy of previously calculated loss gradients. (#1010)
• Fixed bug in ElasticNet attack to apply the confidence parameter when generating adversarial examples. (#995)
• Fixed bug in art.attacks.poisoning.perturbations.image_perturbations.insert_image to correctly transpose input images when channels_first=True. (#1009)
• Fixed bug of missing method compute_loss in PyTorchDeepSpeech, TensorFlowFasterRCNN and BlackBoxClassifier. (#994, #1000)

ART 1.6.0

This release of ART v1.6.0 introduces with the clean-label poisoning attack Bullseye Polytope, a baseline attribute inference attack, and a PyTorch-specific implementation of Adversarial Patch attack with perspective transformation sampling, new evaluation tools in the three different threats types of poisoning, inference and evasion. Furthermore, this release contains the first set of Expectation over Transformation (EoT) preprocessing tools for image processing and natural corruptions.

Added

• Added the Bullseye Polytope clean-label poisoning attack in art.attacks.poisoning.BullseyePolytopeAttackPyTorch (#962)
• Added the Pointwise Differential Training Privacy (PDTP) metric measuring training data membership leakage of trained model in art.metrics.PDTP (#958)
• Added a attribute inference base line attack art.attacks.inference.attribute_inference.AttributeInferenceBaseline defining a minimal attribute inference performance that can be achieved without access to the evaluated model (#956)
• Added a first set of Expectation over Transformation (EoT) preprocessing in art.preprocessing.expectation_over_transformation for image processing and natural image corruptions including brightness, contrast, Gaussian noise, shot noise, and zoom blur. These EoTs enable sampling multiple transformed samples in each forward pass and are fully differentiable for accurate loss gradient calculation in PyTorch and TensorFlow v2. They can be chained together in sequence and are implemented fully framework-specific (#919)
• Added a function for image trigger perturbations blending images (#913)
• Added a method insert_transformed_patch to all adversarial patch attacks art.attacks.evasion.AdversarialPatch* applying adversarial patches onto a perspective transformed square defined by the coordinates of its four corners (#891)
• Added the Adversarial Patch attack framework-specific in PyTorch in art.attacks.evasion.AdversarialPatchPyTorch with additional functionality to support sampling over perspective transformations (#876)

Changed

• Changed handling of NaN values in loss gradients in art.attacks.evasion.FastGradientMethod and art.attacks.evasion.ProjectedGradientDescent* by replacing NaN values with 0.0 and log a warning message. This should prevent losing expensive attack runs in late iterations and still return an adversarial example, but log a warning to alert the user. (#883)
• Changed permitted ranges for eps_step and eps in art.attacks.evasion.ProjectedGradientDescent* to allow eps_step to be larger than eps for all norms, allow eps_step=np.inf to immediately project towards the norm ball or clip_values, and support eps=0.0 to run the attack without any attack budget. The latter two changes are intended to facilitate the verification of attack setups. (#882)
• Changed in the unit tests the marker skipMlFramework to skip_framework and the pytest argument mlFramework to framework (#961)
• Changed art.preprocessing.standardisation_mean_std for standardisation with mean and std to provide extended support for broadcasting by automatically adapting 1-dimensional arrays for mean and std to be broadcastable on NCHW inputs (#839)
• Changed art.estimators.object_detection.PyTorchFasterRCNN.loss_gradient to not overwrite the input label array with tensors (#954)
• Changed and automated the setting of model states by removing method set_learning_phase from all estimators and automating setting the model into the most likely appropriate state for each operation in methods predict (eval mode, training_mode=False) , fit (train mode, training_mode=True) , loss_gradient (eval mode) , class_gradient(eval mode) , etc. The default is defined by a new method argument training_mode which can be changed for example for debugging purposes. An exception are RNN-type models in PyTorch where loss_gradient and class_gradient will run the model in train mode but freeze the model's batch-norm and dropout layers if training_mode=False. (#781)
• Changed art.attacks.evasion.BoundaryAttack in normal (L282) and a suboptimal (L287) termination to return the adversarial example candidate with the smallest norm of the perturbation instead of returning the first adversarial example candidate in its list, this will facilitate the finding the minimum L2 perturbation adversarial examples (#948)
• Changed art.attacks.inference.attribute_inference.AttributeInferenceBlackBox to support one-hot encoded features that have been scaled and lie in-between 0 and 1 instead of just 0 and 1 (#927)
• Changed imports of tensorflow in TensorFlow v1 specific tools to enable backward compatibility and application with TensorFlow v2 (#880)
• Changed optimizer of art.attacks.evasion.AdversarialPatchTensorFlowV2 from SGD to Adam for better performance (#878)
• Changed art.attacks.evasion.BrendelBethgeAttack to include support for numba, following the reference implementation, which leads to great acceleration of the attack (#868)
• Changed art.estimators.classification.ScikitlearnClassifier and all model specific scikit-learn estimators to provide the new argument use_logits to define returning probability or logit predictions in their methods predict (#872)
• Changed metrics clever_t and depending on it clever and clever_u to reduce long runtimes by computing the class gradients of all samples in rand_pool before looping through the batches. To reduce the risk of ResourceExhasutedError, batching is now also applied on rand_pool to compute class gradients on smaller batches of size pool_factor (#762)

Removed

• Removed deprecated argument and property channel_index from all estimators. channel_index has been replaced by channels_first. (#869)

Fixed

• Fixed the criterion of targeted art.attacks.evasion.BoundaryAttack to now correctly check that adversarial predictions are different from the original image prediction during sampling instead of the same (#948)

ART 1.5.3

This release of ART 1.5.3 provides updates to ART 1.5.

Added

[None]

Changed

• Changed argument names of art.attacks.evasion.ImperceptibleASR, art.attacks.evasion.ImperceptibleASRPyTorch and art.attacks.evasion.CarliniWagnerASR where necessary to use the same names in all three attacks. (#955, #959)
• Changed optimisation in art.attacks.evasion.ImperceptibleASRPyTorch to use torch.float64 instead of torch.float32 to prevent NaN as loss value. (#931)
• Changed art.attacks.evasion.ImperceptibleASR to improve the psychoacoustic model and stabilize the imperceptible loss by switching to librosa's STFT and using scalar PSD maximum. (#930)
• Changed art.attacks.evasion.ImperceptibleASR to use periodic window for STFT instead symmetric window option. (#930)
• Changed art.attacks.evasion.ImperceptibleASR with early stopping if loss theta < 0.05 to avoid running into gradients with NaN values. (#930)
• Changed art.attacks.evasion.ImperceptibleASRPyTorch to reset its optimisers for each internal batch in method generate to guarantee the same optimiser performance on each batch, this is especially important for adaptive optimisers. (#917)
• Changed art.attacks.evasion.ImperceptibleASRPyTorch to use torch.stft instead of torchaudio.transforms.Spectrogram to correctly compute the spectrogram. (#914)
• Changed art.estimators.speech_recognition.PyTorchDeepSpeech to freeze batch-norm layers of the Deep Speech model in method loss_gradient to obtain gradients using dataset statistics instead of batch statistics and avoid changing dataset statistics of the batch-norm layers with each call. (#912)

Removed

[None]

Fixed

• Fixed bug of missing argument model in art.estimators.object_detection.TensorFlowFasterRCNN which caused instantiation to fail. (#951)
• Fixed bug of missing square in calculation of loss and class gradients for art.estimators.classification.ScikitlearnSVC using Radial Basis Function (RBF) kernels. (#921)
• Fixed missing support for preprocessing=None in art.estimators.BaseEstimator. (#916)

ART 1.5.2

This release of ART 1.5.2 provides updates to ART 1.5.

Added

• Added new method reset_patch to art.attacks.evasion.adversarial_patch.* to reset patch (#863)
• Added passing kwargs to internal attacks of art.attacks.evasion.AutoAttack (#850)
• Added art.estimators.classification.BlackBoxClassifierNeuralNetwork as black-box classifier for neural network models (#849)
• Added support for channels_first=False for art.attacks.evasion.ShadowAttack in PyTorch (#848)

Changed

• Changed Numpy requirements to be less strict to resolve conflicts in dependencies (#879)
• Changed estimator requirements for art.attacks.evasion.SquareAttack and art.attacks.evasion.SimBA to include NeuralNetworkMixin requiring neural network models (#849)

Removed

[None]

Fixed

• Fixed BaseEstimator.set_params to set preprocessing and preprocessing_defences correctly by accounting for art.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.StandardisationMeanStdPyTorch to maintain correct dtype (#890)
• Fixed type conversion in art.evaluations.security_curve.SecurityCurve to be explicit (#886)
• Fixed dtype in art.attacks.evasion.SquareAttack for norm=2 to maintain correct type (#877)
• Fixed missing CarliniWagnerASR in art.attacks.evasion namespace (#873)
• Fixed support for CUDA i `art.estimators.classification.PyTorchClassifier.loss (#862)
• Fixed bug in art.attacks.evasion.AutoProjectedGradientDescent for 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.ProjectedGradientDescentPyTorch and art.attacks.evasion.ProjectedGradientDescentTensorFlowV2 to use correct labels (#833)

ART 1.5.1

This release of ART 1.5.1 provides updates to ART 1.5.

Added

• Added an option to select to probability values for model extraction attacks in addition to index labels in art.attacks.extraction.CopycatCNN and art.attacks.extraction.KnockoffNets. (#825)
• Added a new notebook demonstrating model extraction attacks and defences. (#825)
• Added art.attacks.evasion.CarliniWagnerASR as a special case of art.attacks.evasion.ImperceptibleASR where max_iter_stage_2=0 skipping the second stage of the ImperceptibleASR. (#784)

Changed

• Changed method generate of art.attacks.evasion.ProjectedGradientDescentPyTorch and art.attacks.evasion.ProjectedGradientDescentTensorFlowV2 to create a copy of the input data to guard the input data from being overwritten by a model that unexpectedly overwrites its input data. This change follows the implementation of art.attacks.evasion.ProjectedGradientDescentNumpy and provides an extra layer of protection against unexpected model behavior. (#805)
• Change numerical precision in art.attacks.evasion.Wasserstein from float to double to reduce numerical overflow in numpy.log and replace input pixel values of 0 with EPS_LOG=10^-10 to prevent division by zero in numpy.log. (#780)
• Changed tqdm imports to use tqdm.auto to automatically run its Jupyter widgets where supported. (#799)
• Improved documentation, argument value checks and added support for index labels in art.attacks.inference.member_ship.LabelOnlyDecisionBoundary. (#790)

Removed

[None]

Fixed

• Fixed bug in art.estimators.classification.KerasClassifier.custom_loss_gradient() to support keras and tensorflow.keras. (#810)
• Fixed bug in art.attacks.evasion.PixelThreshold.generate to correctly scale images in range [0, 255]. (#802)
• Fixed bug in art.attacks.evasion.PixelThreshold to run CMA Evolution Strategy max_iter iterations instead of 1 iteration. (#802)
• Fixed bug in art.estimators.object_detection.PyTorchFasterRCNN by adding missing argument model in super().init. (#789)