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Found 7 papers, 0 papers with code
ReFace: Real-time Adversarial Attacks on Face Recognition Systems
We find that the white-box attack success rate of a pure U-Net ATN falls substantially short of gradient-based attacks like PGD on large face recognition datasets.
Scaled-Time-Attention Robust Edge Network
We demonstrate that STARE is applicable to a variety of applications with improved performance and lower implementation complexity.
TOP: Backdoor Detection in Neural Networks via Transferability of Perturbation
Deep neural networks (DNNs) are vulnerable to "backdoor" poisoning attacks, in which an adversary implants a secret trigger into an otherwise normally functioning model.
Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions
A Pareto GAN leverages extreme value theory and the functional properties of neural networks to learn a distribution that matches the asymptotic behavior of the marginal distributions of the features.
Robot Design With Neural Networks, MILP Solvers and Active Learning
Central to the design of many robot systems and their controllers is solving a constrained blackbox optimization problem.
Universal Lipschitz Approximation in Bounded Depth Neural Networks
Adversarial attacks against machine learning models are a rather hefty obstacle to our increasing reliance on these models.
Limitations of the Lipschitz constant as a defense against adversarial examples
Several recent papers have discussed utilizing Lipschitz constants to limit the susceptibility of neural networks to adversarial examples.
