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Identification of Steganographic Methods Using a Hierarchical CNN Structure  

Kang, Sanghoon (Pukyong National University)
Park, Hanhoon (Pukyong National University)
Park, Jong-Il (Hanyang University)
Kim, Sanhae (Agency of Defense Development)
Publication Information
Journal of the Institute of Convergence Signal Processing / v.20, no.4, 2019 , pp. 205-211 More about this Journal
Abstract
Steganalysis is a technique that aims to detect and recover data hidden by steganography. Steganalytic methods detect hidden data by analyzing visual and statistical distortions caused during data embedding. However, for recovering the hidden data, they need to know which steganographic methods the hidden data has been embedded by. Therefore, we propose a hierarchical convolutional neural network (CNN) structure that identifies a steganographic method applied to an input image through multi-level classification. We trained four base CNNs (each is a binary classifier that determines whether or not a steganographic method has been applied to an input image or which of two different steganographic methods has been applied to an input image) and connected them hierarchically. Experimental results demonstrate that the proposed hierarchical CNN structure can identify four different steganographic methods (LSB, PVD, WOW, and UNIWARD) with an accuracy of 79%.
Keywords
Steganalysis; CNN; Multi-level classification; Hierarchical structure; Secret data recovery;
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