A Spectral View of Adversarially Robust Features A Spectral View of Adversarially Robust Features Garg, Shivam and Sharan, Vatsal and Zhang, Brian Hu and Valiant, Gregory 2018

Paper summary

davidstutz

Garg et al. propose adversarially robust features based on a graph interpretation of the training data. In this graph, training points are connected based on their distance in input space. Robust features are obtained using the eigenvectors of the Laplacian of the graph. It is theoretically shown that these features are robust, based on some assumptions on the graph. For example, the bound obtained on robustness depends on the gap between second and third eigenvalue. Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).