References in zbMATH (referenced in 7 articles )

1. Anirudh, Rushil; Thiagarajan, Jayaraman J.; Kailkhura, Bhavya; Bremer, Peer-Timo: MimicGAN: robust projection onto image manifolds with corruption mimicking (2020)
2. Croce, Francesco; Rauber, Jonas; Hein, Matthias: Scaling up the randomized gradient-free adversarial attack reveals overestimation of robustness using established attacks (2020)
3. Huang, Xiaowei; Kroening, Daniel; Ruan, Wenjie; Sharp, James; Sun, Youcheng; Thamo, Emese; Wu, Min; Yi, Xinping: A survey of safety and trustworthiness of deep neural networks: verification, testing, adversarial attack and defence, and interpretability (2020)
4. Ren Pang, Zheng Zhang, Xiangshan Gao, Zhaohan Xi, Shouling Ji, Peng Cheng, Ting Wang: TROJANZOO: Everything you ever wanted to know about neural backdoors (but were afraid to ask) (2020) arXiv
5. Yaxin Li, Wei Jin, Han Xu, Jiliang Tang: DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses (2020) arXiv
6. Marco Melis, Ambra Demontis, Maura Pintor, Angelo Sotgiu, Battista Biggio: secml: A Python Library for Secure and Explainable Machine Learning (2019) arXiv
7. Jonas Rauber, Wieland Brendel, Matthias Bethge: Foolbox v0.8.0: A Python toolbox to benchmark the robustness of machine learning models (2017) arXiv