Deterministic versus stochastic dynamical classifiers: opposing random adversarial attacks with noise

Abstract

This article explores the comparison between deterministic and stochastic dynamical classifiers in the context of opposing random adversarial attacks using noise. The study provides insights into how these different types of classifiers can be used to mitigate adversarial threats. We analyze the performance of both deterministic and stochastic models under various attack scenarios, highlighting the strengths and weaknesses of each approach.

Lorenzo Giambagli

PostDoc Department of Physics, Freie Universität Berlin

My research interests include Spectral analysis of Deep Neural Network (DNN), Structura Pruning, Bayesian Inference in DNN, Simplicial Complexes Dynamics, Theoretical Neuroscience