Advanced Methods of Detection of the Steganography Content

Hendrych, Jakub; Ličev, Lačezar

doi:10.1007/978-3-030-14907-9_47

Lecture Notes in Electrical Engineering

2019

DOI: 10.1007/978-3-030-14907-9_47

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Advanced Methods of Detection of the Steganography Content

Jakub Hendrych

Lačezar Ličev

Abstract: Steganografie může být využita k nelegálním aktivitám. Proto je velmi důležité být připraven. K detekci steganografického obrázku máme k dispozici techniku známou jako stegoanalýza. Existují různé typy stegoanalýzy v závislosti na tom, zda je znám originální nosič nebo zdali víme, jaký byl použit algoritmus pro vložení tajné zprávy. Z hlediska praktického použití jsou nejdůležitější metody "slepé stagoanalýzy", které zle aplikovat na obrazové soubory a jelikož nemáme originální nosič pro srovnání. Tato doktors… Show more

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2023

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A Markov Chain - Based Method for JPEG Image Steganalysis and Its Application in Combination with Various Machine Learning Algorithms

Prokofieva

Shniperov

2023

jour

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The paper proposes a method of extracting the feature vector of images, which makes it possible to effectively detect the presence of hidden information in JPEG images embedded by various popular steganography tools. This method is based on the usage of the transition probability matrix. The essence of the method for extracting the feature vector of the image is to use the transition probability matrix and apply the image calibration method to improve the accuracy of steganalysis and reduce the number of false positives. For each image from the training and test sets a feature vector is found in this way, the number of elements is 324. Further, the models were trained on the training dataset by each of machine learning methods separately: decision trees with gradient boosting, linear models, k-nearest neighbors, support vector machines, neural networks, and artificial immune systems. To assess the capacity of the models the following metrics were used: accuracy, the rate of the false positive and false negative errors, and the confusion matrix. The results of classification by each of the above methods are given. For training and testing a dataset IStego100K was used, which consists of 208 thousand images of the same size 1024 x 1024 with different quality values in the range from 75 to 95. One of the J-UNIWARD, nsF5, and UERD steganography algorithms was used to embed a hidden message. As a result, we can observe that the proposed approach to extracting the feature vector makes it possible to detect the presence of hidden information embedded by non-adaptive steganography (Steghide, OutGuess and nsF5) in static JPEG images with high accuracy (more than 95%). However, for adaptive steganography methods (J-UNIWARD, UERD) the accuracy is less (about 50-60%).

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A Markov Chain - Based Method for JPEG Image Steganalysis and Its Application in Combination with Various Machine Learning Algorithms

Prokofieva

Shniperov

2023

jour

View full text Add to dashboard Cite

show abstract

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Advanced Methods of Detection of the Steganography Content

Cited by 1 publication

References 47 publications

A Markov Chain - Based Method for JPEG Image Steganalysis and Its Application in Combination with Various Machine Learning Algorithms

A Markov Chain - Based Method for JPEG Image Steganalysis and Its Application in Combination with Various Machine Learning Algorithms

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