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Found 6 papers, 5 papers with code
Wireless Fingerprinting via Deep Learning: The Impact of Confounding Factors
The opportunity for doing so arises due to subtle nonlinear variations across transmitters, even those made by the same manufacturer.
Polarizing Front Ends for Robust CNNs
The vulnerability of deep neural networks to small, adversarially designed perturbations can be attributed to their "excessive linearity."
Robust Wireless Fingerprinting via Complex-Valued Neural Networks
A "wireless fingerprint" which exploits hardware imperfections unique to each device is a potentially powerful tool for wireless security.
Robust Adversarial Learning via Sparsifying Front Ends
We also devise attacks based on the locally linear model that outperform the well-known FGSM attack.
Combating Adversarial Attacks Using Sparse Representations
It is by now well-known that small adversarial perturbations can induce classification errors in deep neural networks (DNNs).
Sparsity-based Defense against Adversarial Attacks on Linear Classifiers
In this paper, we study this phenomenon in the setting of a linear classifier, and show that it is possible to exploit sparsity in natural data to combat $\ell_{\infty}$-bounded adversarial perturbations.
