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Found 4 papers, 3 papers with code
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).
On the information in spike timing: neural codes derived from polychronous groups
There is growing evidence regarding the importance of spike timing in neural information processing, with even a small number of spikes carrying information, but computational models lag significantly behind those for rate coding.
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.
