Reconstructing Network Inputs with Additive Perturbation Signatures

This addresses a security and privacy problem for machine learning model users, but it appears incremental as it builds on existing perturbation-based methods.

The paper tackles the problem of reconstructing secret model inputs from limited access to model outputs and the ability to evaluate additive perturbations, achieving preliminary results that recover a significant amount of information.