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Home > Archive>Volume 32, Issue 9, 2021 >2901-2915. DOI:10.13328/j.cnki.jos.005980
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Spatial Steganalysis of Low Embedding Rate Based on Convolutional Neural Network
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TP309

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National Natural Science Foundation of China (61972142, 61402162, 61772191); Hunan Provincial Natural Science Foundation of China (2017JJ3040); Open Project Program of the National Laboratory of Pattern Recognition (201900017); Science and Technology Key Projects of Hunan Province (2015TP1004, 2016JC2012); Open Research Fund of Key Laboratory of Network Crime Investigation of Hunan Provincial Colleges (2017WLFZZC001)

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    Abstract:

    In recent years, the research of spatial steganalysis based on deep learning has achieved sound results under high embedding rate, but the detection performance under low embedding rate is still not ideal. Therefore, a convolutional neural network structure is proposed, which uses the SRM filter for preprocessing to obtain implicit noise residuals, adopts three convolution layers and designs the size of convolution kernel reasonably, and selects appropriate batch normalization operations and activation functions to improve the network performance. The experimental results show that compared with the existing methods, the proposed network can achieve better detection performance for WOW, S-UNIWARD, and HILL, three common adaptive steganographic algorithms in spatial domain, and significant improvement in detection performance at low embedding rates of 0.2 bpp, 0.1 bpp, and 0.05 bpp. A step-by-step transfer learning method is also designed to further improve the steganalysis effect under low embedding rate conditions.

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沈军,廖鑫,秦拯,刘绪崇.基于卷积神经网络的低嵌入率空域隐写分析.软件学报,2021,32(9):2901-2915

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History
  • Received:June 18,2019
  • Revised:September 23,2019
  • Online: April 21,2020
  • Published: September 06,2021
Links:Institute of Software, CASChina Computer FederationCASNSFC
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