Certifiers make neural networks vulnerable to availability attacks
To achieve reliable, robust, and safe AI systems, it is vital to implement fallback strategies when AI predictions cannot be trusted. Certifiers for neural networks are a reliable way to check the robustness of these predictions. They guarantee for some predictions that a certain class of manipulati...
Hlavní autoři: | Lorenz, T, Kwiatkowska, M, Fritz, M |
---|---|
Médium: | Conference item |
Jazyk: | English |
Vydáno: |
Association for Computing Machinery
2023
|
Podobné jednotky
-
FullCert: deterministic end-to-end certification for training and inference of neural networks
Autor: Lorenz, T, a další
Vydáno: (2024) -
Certified Robustness to Text Adversarial Attacks by Randomized [MASK]
Autor: Jiehang Zeng, a další
Vydáno: (2023-06-01) -
Attack Vulnerability of Network Controllability.
Autor: Zhe-Ming Lu, a další
Vydáno: (2016-01-01) -
Bayesian inference with certifiable adversarial robustness
Autor: Wicker, M, a další
Vydáno: (2021) -
Vulnerability analysis on noise-injection based hardware attack on deep neural networks
Autor: Liu, Wenye, a další
Vydáno: (2020)