title: ÐÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¸ ÑÑÐ°Ð²Ð½Ð¸ÑÐµÐ»ÑÐ½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð· ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð¸Ñ Ð¿Ð°ÑÑÐ¸Ð²Ð½Ð¾Ð³Ð¾ ÑÑÐµÐ³Ð¾Ð°Ð½Ð°Ð»Ð¸Ð·Ð° ÑÐ°ÑÑÑÐ¾Ð²ÑÑ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Research and comparative analysis of machine learning classification models for performing bitmap passive steganalysis
Creators: name: Ð¨Ð°Ð»Ð¸Ð½Ð°, ÐÐºÑÐ°Ð½Ð°
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½ÑÐµ Ð¼Ð¾Ð´ÐµÐ»Ð¸
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÑÑÐµÐ³Ð¾Ð°Ð½Ð°Ð»Ð¸Ð·
ÑÐ°ÑÑÑÐ¾Ð²ÑÐµ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ
classification models
machine learning
steganalysis
bitmap images

description: Ð¦ÐµÐ»ÑÑ Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÐµÑÑÑ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð·Ð°Ð´Ð°ÑÐ¸ Ð²ÑÑÐ²Ð»ÐµÐ½Ð¸Ñ Ð½Ð°Ð»Ð¸ÑÐ¸Ñ Ð² ÑÐ°ÑÑÑÐ¾Ð²ÑÑ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸ÑÑ ÑÐºÑÑÑÐ¾Ð¹ Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¸. Ð Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½ ÑÑÐ°Ð²Ð½Ð¸ÑÐµÐ»ÑÐ½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð· Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ ÑÐ°ÑÑÑÐ¾Ð²ÑÑ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ð½Ð° Ð¿ÑÐµÐ´Ð¼ÐµÑ Ð½Ð°Ð»Ð¸ÑÐ¸Ñ Ð² Ð½Ð¸Ñ ÑÐºÑÑÑÑÑ ÑÐ¾Ð¾Ð±ÑÐµÐ½Ð¸Ð¹. Ð ÑÐ°Ð±Ð¾ÑÐµ ÑÐ°ÑÑÐ¼Ð°ÑÑÐ¸Ð²Ð°ÑÑÑÑ ÐºÐ»Ð°ÑÑÐ¸ÑÐµÑÐºÐ¸Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸: Ð¼ÐµÑÐ¾Ð´ k-Ð±Ð»Ð¸Ð¶Ð°Ð¹ÑÐ¸Ñ ÑÐ¾ÑÐµÐ´ÐµÐ¹, Ð´ÐµÑÐµÐ²Ð¾ ÑÐµÑÐµÐ½Ð¸Ð¹, Ð¼ÐµÑÐ¾Ð´ Ð¾Ð¿Ð¾ÑÐ½ÑÑ Ð²ÐµÐºÑÐ¾ÑÐ¾Ð², ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾Ñ ÐÐ°Ð¹ÐµÑÐ°, Ð»Ð¾Ð³Ð¸ÑÑÐ¸ÑÐµÑÐºÐ°Ñ ÑÐµÐ³ÑÐµÑÑÐ¸Ñ, Ð° ÑÐ°ÐºÐ¶Ðµ Ð°Ð½ÑÐ°Ð¼Ð±Ð»ÐµÐ²ÑÐµ Ð¼ÐµÑÐ¾Ð´Ñ, ÑÐ°ÐºÐ¸Ðµ ÐºÐ°Ðº ÑÐ»ÑÑÐ°Ð¹Ð½ÑÐ¹ Ð»ÐµÑ, Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð°Ð´Ð°Ð¿ÑÐ¸Ð²Ð½Ð¾Ð³Ð¾ Ð±ÑÑÑÐ¸Ð½Ð³Ð° Ð¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ HistGradientBoosting. Ð Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ð¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÑÑÑ Ð½ÐµÑÐºÐ¾Ð»ÑÐºÐ¾ Ð½Ð°Ð±Ð¾ÑÐ¾Ð² Ð´Ð°Ð½Ð½ÑÑ, ÑÑÐ¾ÑÐ¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÑ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð²Ð½ÐµÐ´ÑÐµÐ½Ð¸Ñ, ÑÐ°ÐºÐ¸Ñ ÐºÐ°Ðº Ð¼ÐµÑÐ¾Ð´Ð° Ð¸Ð·Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð½Ð°Ð¸Ð¼ÐµÐ½ÑÑÐµÐ³Ð¾ Ð·Ð½Ð°ÑÐ°ÑÐµÐ³Ð¾ Ð±Ð¸ÑÐ°, Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° ÐÐµÐ½ÑÐ°Ð¼Ð°, Ð° ÑÐ°ÐºÐ¶Ðµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¸ Ð² Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸ÑÑ: Ð¼ÐµÑÐ¾Ð´Ð° ÑÐ°ÑÑÐµÑÐ° Ð¾ÑÐ½Ð¾ÑÐ¸ÑÐµÐ»ÑÐ½Ð¾Ð³Ð¾ ÑÐ¸ÑÐ»Ð° Ð¿ÐµÑÐµÑÐ¾Ð´Ð¾Ð² Ð² Ð¼Ð»Ð°Ð´ÑÐ¸Ñ Ð±Ð¸ÑÐ°Ñ Ð¸ Ð¼ÐµÑÐ¾Ð´ Ð²ÑÑÐ¸ÑÐ»ÐµÐ½Ð¸Ñ Ð¼Ð°ÑÑÐ¸ÑÑ ÑÐ¼ÐµÐ¶Ð½Ð¾ÑÑÐ¸ ÑÐ°Ð·Ð½Ð¾ÑÑÐ¸ Ð¿Ð¸ÐºÑÐµÐ»ÐµÐ¹.Â  ÐÐ»Ñ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ñ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¿ÑÐ¸Ð²Ð¾Ð´ÑÑÑÑ Ð¾ÑÐµÐ½ÐºÐ° ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ Ð¸ Ð¾ÑÐµÐ½ÐºÐ° F1 ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸. Ð ÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ Ð¼Ð¾Ð³ÑÑ Ð±ÑÑÑ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ñ Ð´Ð»Ñ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ð³Ð¾ Ð¼Ð¾Ð´ÑÐ»Ñ Ð¿Ð¾ Ð²ÑÑÐ²Ð»ÐµÐ½Ð¸Ñ Ð½Ð°Ð»Ð¸ÑÐ¸Ñ ÑÐºÑÑÑÐ¾Ð¹ Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¸ Ð² ÑÐ°ÑÑÑÐ¾Ð²ÑÑ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸ÑÑ.
description: The purpose of this work is to compare classification models of machine learning for the task of detecting the presence of hidden information in bitmap images. This paper presents a comparative analysis of machine learning models for classifying bitmap images for the presence of hidden messages in them. The paper considers classical models: the k-nearest neighbors method, decision tree, support vector machine, Bayes classifier, logistic regression, as well as ensemble methods such as random forest, adaptive boosting algorithm and HistGradientBoosting algorithm. The study uses several data sets generated using implementation algorithms, such as the method of changing the smallest seed bit, the Benham algorithm, as well as algorithms for detecting information in images: a method for calculating the relative number of transitions in the lower bits and a method for calculating the subtractive pixel adjacency matrix.Â  To compare the effectiveness of the models, an accuracy score and an F1-score are given. The results of this work can be used to develop a software module for detecting the presence of hidden information in bitmap images.