Neural Network Steganography Using Extractor Matching

EasyChair Preprint 11244, version 1

Abstract

Neural networks have been applied in various fields, including steganography (called neural network steganography). The network used for secret data extrac-tion is called the extractor. This paper proposes a neural network steganography scheme using extractor matching. In our scheme, the extractor is a publicly avail-able normal network possessed by the receiver, which is used for conventional intelligent tasks. Sender connects extractor to another neural network (called cover network), and then trains the connected network to guarantee correctly data extraction without decreasing the performance of the original task of cover network. During the process of training, the parameters of extractor remain un-changed. Specifically, these network parameters are obtained using an extraction key. The receiver can correctly extract secret data with the help of correct extrac-tion key, while an incorrect key will fail to extract secret data. The feasibility of our scheme is demonstrated in experiments.

Keyphrases: Extractor matching, Steganography, neural networks

@booklet{EasyChair:11244,
  author    = {Yunfei Xie and Zichi Wang},
  title     = {Neural Network Steganography Using Extractor Matching},
  howpublished = {EasyChair Preprint 11244},
  year      = {EasyChair, 2023}}