Reconstructing Network Inputs with Additive Perturbation Signatures

Abstract

In this work, we present preliminary results demonstrating the ability to recover a significant amount of information about secret model inputs given only very limited access to model outputs and the ability evaluate the model on additive perturbations to the input.

References in corpus (1)

• Adversarial Neural Network Inversion via Auxiliary Knowledge Alignment