DAVIDSTUTZ

21thJULY2018

$\min_g \sum_i \max_{\|r^{(i)}\|\leq c} l(g(x_i + r^{(i)}), y_i)$
where $g$ ranges over expressible functions and $(x_i, y_i)$ is a training sample. In the remainder of the paper, Huang et al. Address the problem of efficiently computing $r^{(i)}$ – i.e. a strong adversarial example based on the current state of the network – and subsequently updating the weights of the network by computing the gradient of the augmented loss. Details can be found in the paper.