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...
Hoofdauteurs: | Lorenz, T, Kwiatkowska, M, Fritz, M |
---|---|
Formaat: | Conference item |
Taal: | English |
Gepubliceerd in: |
Association for Computing Machinery
2023
|
Gelijkaardige items
-
FullCert: deterministic end-to-end certification for training and inference of neural networks
door: Lorenz, T, et al.
Gepubliceerd in: (2024) -
Certified Robustness to Text Adversarial Attacks by Randomized [MASK]
door: Jiehang Zeng, et al.
Gepubliceerd in: (2023-06-01) -
Attack Vulnerability of Network Controllability.
door: Zhe-Ming Lu, et al.
Gepubliceerd in: (2016-01-01) -
Bayesian inference with certifiable adversarial robustness
door: Wicker, M, et al.
Gepubliceerd in: (2021) -
Vulnerability analysis on noise-injection based hardware attack on deep neural networks
door: Liu, Wenye, et al.
Gepubliceerd in: (2020)