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Volume: 29 | Article ID: art00009
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Histogram Layer, Moving Convolutional Neural Networks Towards Feature-Based Steganalysis
  DOI :  10.2352/ISSN.2470-1173.2017.7.MWSF-325  Published OnlineJanuary 2017
Abstract

Feature-based steganalysis has been an integral tool for detecting the presence of steganography in communication channels for a long time. In this paper, we explore the possibility to utilize powerful optimization algorithms available in convolutional neural network packages to optimize the design of rich features. To this end, we implemented a new layer that simulates the formation of histograms from truncated and quantized noise residuals computed by convolution. Our goal is to show the potential to compactify and further optimize existing features, such as the projection spatial rich model (PSRM).

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Vahid Sedighi, Jessica Fridrich, "Histogram Layer, Moving Convolutional Neural Networks Towards Feature-Based Steganalysisin Proc. IS&T Int’l. Symp. on Electronic Imaging: Media Watermarking, Security, and Forensics,  2017,  pp 50 - 55,  https://doi.org/10.2352/ISSN.2470-1173.2017.7.MWSF-325

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Copyright © Society for Imaging Science and Technology 2017
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Electronic Imaging
2470-1173
Society for Imaging Science and Technology