# PixelSteganalysis: Pixel-wise Hidden Information Removal with Low Visual Degradation

## TL;DR

This paper introduces a novel deep learning-based steganalysis method that effectively removes hidden information from images at the pixel level, outperforming traditional methods and ensuring the integrity of original images.

## Contribution

It is the first to disable DL-based steganography by restoring original pixel and edge distributions, improving detection and removal of hidden data.

## Key findings

- 10-20% improvement in decoded rate
- 10-20% enhancement in destruction rate (DT)
- Effective pixel-wise removal of hidden information

## Abstract

Recently, the field of steganography has experienced rapid developments based on deep learning (DL). DL based steganography distributes secret information over all the available bits of the cover image, thereby posing difficulties in using conventional steganalysis methods to detect, extract or remove hidden secret images. However, our proposed framework is the first to effectively disable covert communications and transactions that use DL based steganography. We propose a DL based steganalysis technique that effectively removes secret images by restoring the distribution of the original images. We formulate a problem and address it by exploiting sophisticated pixel distributions and an edge distribution of images by using a deep neural network. Based on the given information, we remove the hidden secret information at the pixel level. We evaluate our technique by comparing it with conventional steganalysis methods using three public benchmarks. As the decoding method of DL based steganography is approximate (lossy) and is different from the decoding method of conventional steganography, we also introduce a new quantitative metric called the destruction rate (DT). The experimental results demonstrate performance improvements of 10-20% in both the decoded rate and the DT.