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Found 4 papers, 3 papers with code
$\ell_\infty$-Robustness and Beyond: Unleashing Efficient Adversarial Training
Our experimental results indicate that our approach speeds up adversarial training by 2-3 times, while experiencing a small reduction in the clean and robust accuracy.
AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows
Deep learning classifiers are susceptible to well-crafted, imperceptible variations of their inputs, known as adversarial attacks.
Black-box Adversarial Example Generation with Normalizing Flows
Deep neural network classifiers suffer from adversarial vulnerability: well-crafted, unnoticeable changes to the input data can affect the classifier decision.
Invertible Generative Modeling using Linear Rational Splines
The significant advantage of such models is their easy-to-compute inverse.
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