The Comparison of Classifiers in Image Steganalysis

Abstract

In this paper, proposed steganalytic method utilized for the detection of secret message is based on extraction of statistical features from cover and stego images in JPEG file format together with calibration technique. The steganalyzer concept uses Support Vector Machines (SVM) classification or Bayes classifier for training a model that is later used by the same steganalyzer in order to identify between a clean (cover) and stego image. The aim of the paper was to compare detection accuracy (ACR) of the trained models for two types of classifiers: Support Vector Machines and Bayes classifier. In this paper, five models created between cover and stego images (images obtained by nsF5, Model Based 1, Model Based 2, Modulo Histogram Fitting with Dead Zone and Pertubed Quantization steganographic method) was tested.

Analysis of Steganographic Methods in DCT Domain

“…Moreover, an important attribute of steganographic methods is a minimization of impact to image statistical parameters after an embedding process, since the greater impact a method has, the more vulnerable to detect by an attacker it is [2]. In other words, the goal is to make a method more resistant to steganalysis [3]. In general, steganalytic technique extracts statistical parameters from a testing image to evaluate them by the previously trained model.…”

Image steganography with using QR code and cryptography

“…These methods are based on the strong complex mathematical calculations. Classifier is the main part of such tools and an efficiency of steganalytic methods is also dependent on a type of classifier [8,9].…”