# Unsupervised Steganalysis Based on Artificial Training Sets

## TL;DR

This paper introduces an unsupervised steganalysis approach that uses artificial training sets and supervised classification, effectively detecting stego images across various datasets and embedding algorithms without needing a dedicated training database.

## Contribution

It presents a formal framework for unsupervised steganalysis that overcomes cover source mismatch and demonstrates superior performance over previous methods using RichModels.

## Key findings

- Outperforms previous RichModel-based methods in most tested cases.
- Removes the need for a training database when the embedding algorithm and bit rate are known.
- Applicable to various image datasets and embedding algorithms.

## Abstract

In this paper, an unsupervised steganalysis method that combines artificial training setsand supervised classification is proposed. We provide a formal framework for unsupervisedclassification of stego and cover images in the typical situation of targeted steganalysis (i.e.,for a known algorithm and approximate embedding bit rate). We also present a completeset of experiments using 1) eight different image databases, 2) image features based on RichModels, and 3) three different embedding algorithms: Least Significant Bit (LSB) matching,Highly undetectable steganography (HUGO) and Wavelet Obtained Weights (WOW). Weshow that the experimental results outperform previous methods based on Rich Models inthe majority of the tested cases. At the same time, the proposed approach bypasses theproblem of Cover Source Mismatch -when the embedding algorithm and bit rate are known-, since it removes the need of a training database when we have a large enough testing set.Furthermore, we provide a generic proof of the proposed framework in the machine learningcontext. Hence, the results of this paper could be extended to other classification problemssimilar to steganalysis.