Generative focused feedback residual networks for image steganalysis and hidden information reconstruction

Image steganalysis is the process of detecting the presence of hidden information in an image. Existing image steganalysis methods cannot fully extract hidden image information even if the presence of hidden information is successfully detected, resulting in either poor detection performance or inability to reconstruct hidden information. This paper proposes a generative image steganalysis algorithm based on focused feedback residual convolutional neural network for simultaneous detection and extraction of hidden information. First, a possible stego image was processed by a preprocessing network consisting of several convolutional layers and two fresh focus modules, and several enhanced feature maps were output. Then, the resulting enhanced feature maps were fetched into both a classification network and a reconstruction network. The classification network identified whether the feature maps came from a stego image or a simple cover image. The reconstruction network, consisting of some layers of convolutional units, pixel shufflers and feedback residual modules, completed the reconstruction of hidden information. Experimental results show that the proposed image steganalysis algorithm can obtain state-of-the-art results in terms of detection rate and hidden information reconstruction compared with classical rich models and several recent deep learning-based methods.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a deep learning-based image steganalysis algorithm that can simultaneously detect and extract hidden information.