Where our proposed approach differs is that we train models using first-order derivatives of the training loss as part of a regularization term, necessitating second-order derivatives for computing the gradient.
and In other words, minimising local noise sensitivity is equivalent to minimising the Frobenius norm of the Jacobean matrix of partial derivatives of the model outputs wrt its inputs.