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http://dx.doi.org/10.9717/kmms.2020.24.4.550

Hierarchical CNN-Based Senary Classification of Steganographic Algorithms  

Kang, Sanhoon (Dept. of Electronic Engineering, Pukyong National University)
Park, Hanhoon (Dept. of Electronic Engineering, Pukyong National University)
Publication Information
Journal of Korea Multimedia Society / v.24, no.4, 2021 , pp. 550-557 More about this Journal
Abstract
Image steganalysis is a technique for detecting images with steganographic algorithms applied, called stego images. With state-of-the-art CNN-based steganalysis methods, we can detect stego images with high accuracy, but it is not possible to know which steganographic algorithm is used. Identifying stego images is essential for extracting embedded data. In this paper, as the first step for extracting data from stego images, we propose a hierarchical CNN structure for senary classification of steganographic algorithms. The hierarchical CNN structure consists of multiple CNN networks which are trained to classify each steganographic algorithm and performs binary or ternary classification. Thus, it classifies multiple steganogrphic algorithms hierarchically and stepwise, rather than classifying them at the same time. In experiments of comparing with several conventional methods, including those of classifying multiple steganographic algorithms at the same time, it is verified that using the hierarchical CNN structure can greatly improve the classification accuracy.
Keywords
Steganography; Steganalysis; Hierarchical CNN; Senary Classification;
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