Saeid Asgari Taghanaki

A Kernelized Manifold Mapping to Diminish the Effect of Adversarial Perturbations

The linear and non-flexible nature of deep convolutional models makes them vulnerable to carefully crafted adversarial perturbations. To tackle this problem, we propose a non-linear radial basis convolutional feature mapping by learning a Mahalanobis-like distance function. Our method then maps the…