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Found 7 papers, 5 papers with code
The Devil's Advocate: Shattering the Illusion of Unexploitable Data using Diffusion Models
Protecting personal data against the exploitation of machine learning models is of paramount importance.
COLLIDER: A Robust Training Framework for Backdoor Data
We show the effectiveness of the proposed method for robust training of DNNs on various poisoned datasets, reducing the backdoor success rate significantly.
Adversarial Coreset Selection for Efficient Robust Training
By leveraging the theory of coreset selection, we show how selecting a small subset of training data provides a principled approach to reducing the time complexity of robust training.
$\ell_\infty$-Robustness and Beyond: Unleashing Efficient Adversarial Training
Neural networks are vulnerable to adversarial attacks: adding well-crafted, imperceptible perturbations to their input can modify their output.
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.
