The following notebook is a basic demonstration of our framework:
For an advanced demonstration, follow the link to this notebook:
This repository contains the datasets and code for the paper.
Trust-ML is a machine learning-driven adversarial data generator that introduces naturally occurring distortions to the original dataset to generate an adversarial subset. This framework provides a custom mix of distortions for evaluating robustness of image classification models against both true negatives and false positives. Our framework enables users to audit their algorithms with customizable distortions. With the help of RLAB, we can generate more effective and efficient adversarial samples than other distortion-based benchmarks. In our experiments, we demonstrate that samples generated with our framework cause greater accuracy degradation of state-of-the-art adversarial robustness methods (as tracked by RobustBench) than ImageNet-C and CIFAR-10-C.
Refer to the docs for documentation and installation of Trust-ML.