Adversarially Pretrained Transformers may be Universally Robust In-Context Learners
Authors: Soichiro Kumano, Hiroshi Kera, Toshihiko Yamasaki | Published: 2025-05-20
Relationship between Robustness and Privacy
ModSec-AdvLearn: Countering Adversarial SQL Injections with Robust Machine Learning
Authors: Giuseppe Floris, Christian Scano, Biagio Montaruli, Luca Demetrio, Andrea Valenza, Luca Compagna, Davide Ariu, Luca Piras, Davide Balzarotti, Battista Biggio | Published: 2023-08-09 | Updated: 2025-05-21
Robustness, Privacy, and Generalization of Adversarial Training
Authors: Fengxiang He, Shaopeng Fu, Bohan Wang, Dacheng Tao | Published: 2020-12-25
Adversarial Robustness of Stabilized NeuralODEs Might be from Obfuscated Gradients
Authors: Yifei Huang, Yaodong Yu, Hongyang Zhang, Yi Ma, Yuan Yao | Published: 2020-09-28 | Updated: 2021-06-02
A Robust graph attention network with dynamic adjusted Graph
Authors: Xianchen Zhou, Yaoyun Zeng, Hongxia Wang | Published: 2020-09-28 | Updated: 2022-08-04
Where Does the Robustness Come from? A Study of the Transformation-based Ensemble Defence
Authors: Chang Liao, Yao Cheng, Chengfang Fang, Jie Shi | Published: 2020-09-28 | Updated: 2020-10-08
Differentially Private Adversarial Robustness Through Randomized Perturbations
Authors: Nan Xu, Oluwaseyi Feyisetan, Abhinav Aggarwal, Zekun Xu, Nathanael Teissier | Published: 2020-09-27
Interpretation of Neural Networks is Fragile
Authors: Amirata Ghorbani, Abubakar Abid, James Zou | Published: 2017-10-29 | Updated: 2018-11-06
Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation
Authors: Matthias Hein, Maksym Andriushchenko | Published: 2017-05-23 | Updated: 2017-11-05