adversarial-robustness-toolbox
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Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams
> TensorFlow Addons (TFA) has ended development and introduction of new features. > TFA has entered a minimal maintenance and release mode until a planned end of life in May...
**Describe the bug** The Wasserstein attack produces NaNs in the output. **To Reproduce** I believe I've produced a minimal example of the issue. You can run it on Google's Colab...
Hi, When using JSMA method, I found that **the implementation of adversarial saliency map of this toolbox is slightly different from the original paper:** **In this toolbox,** corresponding implementation in...
### Discussed in https://github.com/Trusted-AI/adversarial-robustness-toolbox/discussions/1950 Originally posted by **sr1998** December 3, 2022 Hi, is it possible for the library to support newer versions of scikit-learn? At the moment it supports scikit-learn=0.22.2...
**Is your feature request related to a problem? Please describe.** I was benchmarking the performance of some black-box attacks and noticed that the Square Attack issued about 50% duplicated queries....
Since yolov5 had a segmentation branch, and how to use art on the segmentation model of yolov5? Is there any plan for adding this support on yolov5-seg?
Currently, many ART estimators and tools do not have progress bars to indicate how quickly they are training, or to display useful information such as loss and accuracy. With ART...
Many tests use the PyTorch image estimator defined in the test utils. By default this estimator does _not_ use logits, e.g. the function signature is: `get_image_classifier_pt(from_logits=False, load_init=True, use_maxpool=True)` However, the...
**Describe the bug** Initializing an attack on a PyTorchRegressor throws an EstimatorError with missing words in the message: ```EstimatorError: AutoProjectedGradientDescent requires an estimator derived from and and , the provided...
Currently PyTorchClassifier determines the internal structure of the provided model via self._model._modules.items(). This is quite a brittle approach as it it purely dependant on how the layers were declared in...