Name
Abstract | ||
|---|---|---|
Adversarial examples have become an indisputable threat to the security of modern AI systems based on deep neural networks (DNNs). The Adversarial Robustness Toolbox (ART) is a Python library designed to support researchers and developers in creating novel defence techniques, as well as in deploying practical defences of real-world AI systems. Researchers can use ART to benchmark novel defences against the state-of-the-art. For developers, the library provides interfaces which support the composition of comprehensive defence systems using individual methods as building blocks. The Adversarial Robustness Toolbox supports machine learning models (and deep neural networks (DNNs) specifically) implemented in any of the most popular deep learning frameworks (TensorFlow, Keras, PyTorch). Currently, the library is primarily intended to improve the adversarial robustness of visual recognition systems, however, future releases that will comprise adaptations to other data modes (such as speech, text or time series) are envisioned. The ART source code is released (this https URL) under an MIT license. The release includes code examples and extensive documentation (this http URL) to help researchers and developers get quickly started. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Learning | Software engineering,Computer science,Source code,Toolbox,Robustness (computer science),MIT License,Artificial intelligence,Deep learning,Documentation,Python (programming language),Adversarial system |
DocType | Volume | Citations |
Journal | abs/1807.01069 | 2 |
PageRank | References | Authors |
0.37 | 20 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maria-Irina Nicolae | 1 | 2 | 0.71 |
| Mathieu Sinn | 2 | 55 | 10.41 |
| Ngoc Minh Tran | 3 | 59 | 5.08 |
| Ambrish Rawat | 4 | 10 | 1.91 |
| Martin Wistuba | 5 | 154 | 19.66 |
| Valentina Zantedeschi | 6 | 52 | 4.90 |
| Ian Molloy | 7 | 733 | 38.81 |
| Benjamin Edwards | 8 | 19 | 3.77 |