Comparative Study of Three Steganographic Methods Using a Chaotic System and Their Universal Steganalysis Based on Three Feature Vectors.

Dalia Battikh,Bassem Bakhache,Mohamad Khalil,Thang Manh Hoang,Safwan El Assad,Olivier Deforges

doi:10.3390/e21080748

Abstract

In this paper, we firstly study the security enhancement of three steganographic methods by using a proposed chaotic system. The first method, namely the Enhanced Edge Adaptive Image Steganography Based on LSB Matching Revisited (EEALSBMR), is present in the spatial domain. The two other methods, the Enhanced Discrete Cosine Transform (EDCT) and Enhanced Discrete Wavelet transform (EDWT), are present in the frequency domain. The chaotic system is extremely robust and consists of a strong chaotic generator and a 2-D Cat map. Its main role is to secure the content of a message in case a message is detected. Secondly, three blind steganalysis methods, based on multi-resolution wavelet decomposition, are used to detect whether an embedded message is hidden in the tested image (stego image) or not (cover image). The steganalysis approach is based on the hypothesis that message-embedding schemes leave statistical evidence or structure in images that can be exploited for detection. The simulation results show that the Support Vector Machine (SVM) classifier and the Fisher Linear Discriminant (FLD) cannot distinguish between cover and stego images if the message size is smaller than 20% in the EEALSBMR steganographic method and if the message size is smaller than 15% in the EDCT steganographic method. However, SVM and FLD can distinguish between cover and stego images with reasonable accuracy in the EDWT steganographic method, irrespective of the message size.

Highlights

Steganography is an increasingly important security domain; it aims to hide a message in digital cover media without causing perceptual degradation
We present some experimental results that were obtained from the studied steganalysis system that was applied to the enhanced steganographic methods in the spatial and frequency domain
The classification results provided in Tables 15–20 for the Enhanced Discrete Cosine Transform (EDCT) insertion method show that with the Fisher Linear Discriminant (FLD) classifier, when the insertion rate is equal to or higher than 20%, steganalysis is very effective with Shi features and Moulin features, but it is less effective with Farid features

Summary

Introduction

Steganography is an increasingly important security domain; it aims to hide a message (secret information) in digital cover media without causing perceptual degradation (in this study, we use images as cover media). Pixels are directly used to hide secret messages; these techniques are normally easy to implement and have a high capacity. They are not generally robust against statistical attacks [1,2]. Wavelet Transform), are used to hide secret data. These techniques are complex, but they are more robust against steganalysis (to noise and to image processing)

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Results

Discussion

Conclusion