Lipschitz Bounds and Provably Robust Training by Laplacian Smoothing

v1v2v3 (latest)

Lipschitz Bounds and Provably Robust Training by Laplacian Smoothing

5 June 2020

Vishaal Krishnan

Abed AlRahman Al Makdah

Fabio Pasqualetti

ArXiv (abs)PDF HTML

Papers citing "Lipschitz Bounds and Provably Robust Training by Laplacian Smoothing"

6 / 6 papers shown

Title
Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems Ping He Lorenzo Cavallaro Shouling Ji AAML 209 0 0 23 Jan 2025
A Quantitative Geometric Approach to Neural-Network Smoothness Zehao Wang Gautam Prakriya S. Jha 112 13 0 02 Mar 2022
Adversarial for Good? How the Adversarial ML Community's Values Impede Socially Beneficial Uses of Attacks Kendra Albert Maggie K. Delano B. Kulynych Ramnath Kumar AAML 129 5 0 11 Jul 2021
Robust Implicit Networks via Non-Euclidean Contractions Saber Jafarpour A. Davydov A. Proskurnikov Francesco Bullo 155 43 0 06 Jun 2021
Robust Adversarial Classification via Abstaining Abed AlRahman Al Makdah Vaibhav Katewa Fabio Pasqualetti 44 0 0 06 Apr 2021
CLIP: Cheap Lipschitz Training of Neural Networks Leon Bungert René Raab Tim Roith Leo Schwinn Daniel Tenbrinck 59 33 0 23 Mar 2021