What is robustness neural network?
Observing that real-world inputs to neural networks are drawn from non-adversarial probability distributions, we propose a novel notion of robustness: probabilistic robustness, which requires the neural network to be robust with at least (1 – ‘e) probability with respect to the input distribution.
What is CleverHans?
CleverHans is a software library that provides standardized reference implementations of adversarial example construction techniques and adversarial training. The library may be used to develop more robust machine learning models and to provide standardized benchmarks of models’ performance in the adversarial setting.
Does interpretability of neural networks imply adversarial robustness?
We demonstrate that training the networks to have interpretable gradients improves their robustness to adversarial perturbations. The results indicate that the interpretability of the model gradients is a crucial factor for adversarial robustness.
What do you need to know about adversarial robustness?
This document assumes some degree of familiarity with basic deep learning, e.g., the basics of optimization, gradient descent, deep networks, etc (to the degree that is typically covered in an early graduate-level course on machine learning), plus some basic familiarity with PyTorch.
Which is the best version of Python for adversarial robustness?
Specifically, all example here use Python 3.7 (though should be compatible with 3.6), and make use of the following libraries: Ideally, the precise version number should not matter, and the approach should be compatible with e.g. pytorch 0.4.x (but not earlier), earlier versions of pillow, etc.
How many robustness metrics does art support?
ART supports 3 robustness metrics, 1 certification and 1 verification metric. It also supports multiple estimators and details about the same can be found here. Learn how to set up the toolbox and find example notebooks in the user guide, along with documentation of the modules attacks, defenses, metrics and more here.