These attributes claim the researchers, make EagerPy easier and safer to work with than the underlying framework-specific APIs. Despite these changes and improvements, the team behind EagerPy made sure that the EagerPy API follows the standards set by NumPy, PyTorch, and JAX.

Even todays most advanced machine learning models are easily fooled by almost imperceptible perturbations of their inputs. Foolbox is a new Python package to generate such adversarial perturbations and to quantify and compare the robustness of machine learning models. It is build around the idea that the most comparable robustness measure is the minimum perturbation needed to craft an adversarial example. To this end, Foolbox provides reference implementations of most published adversarial attack methods alongside some new ones, all of which perform internal hyperparameter tuning to find the minimum adversarial perturbation. Additionally, Foolbox interfaces with most popular deep learning frameworks such as PyTorch, Keras, TensorFlow, Theano and MXNet and allows different adversarial criteria such as targeted misclassification and top-k misclassification as well as different distance measures. The code is licensed under the MIT license and is openly available at https://github.com/bethgelab/foolbox . The most up-to-date documentation can be found at http://foolbox.readthedocs.io .

Would you like to join the merry band of researchers breaking machine learning models? A trio of German researchers has published a tool designed to make it easier to craft adversarial models when you're attacking a "black box". Unlike adversarial models that attack AIs "from the inside", attacks developed for black boxes could be used against closed system like autonomous cars, security (facial recognition, for example), or speech recognition (Alexa or Cortana). The tool, called Foolbox, is currently under review for presentation at next year's International Conference on Learning Representations (kicking off at the end of April). Wieland Brendel, Jonas Rauber and Matthias Bethge of the Eberhard Karls University Tubingen, Germany explained at arXiv that Foolbox is a "decision-based" attack called a boundary attack which "starts from a large adversarial perturbation and then seeks to reduce the perturbation while staying adversarial".