Specific Testing Sample Steganalysis

doi:10.13328/j.cnki.jos.005411

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2025-4-21- 15

Home > Archive>Volume 29, Issue 4, 2018 >987-1001. DOI:10.13328/j.cnki.jos.005411

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Specific Testing Sample Steganalysis
DOI:
                        10.13328/j.cnki.jos.005411
                    
Author:
                        ZHANG Yi-WeiZHANG Yi-Wei
School of Information Science and Technology, University of Science and Technology of China, Hefei 230027, China;Key Laboratory of Electromagnetic Spatial Information of the Chinese Academy of Sciences(University of Science and Technology of China), Hefei 230027, China
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ZHANG Wei-MingZHANG Wei-Ming
School of Information Science and Technology, University of Science and Technology of China, Hefei 230027, China;Key Laboratory of Electromagnetic Spatial Information of the Chinese Academy of Sciences(University of Science and Technology of China), Hefei 230027, China
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YU Neng-HaiYU Neng-Hai
School of Information Science and Technology, University of Science and Technology of China, Hefei 230027, China;Key Laboratory of Electromagnetic Spatial Information of the Chinese Academy of Sciences(University of Science and Technology of China), Hefei 230027, China
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Fund Project:National Natural Science Foundation of China (U1636201, 61572452)

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Abstract:

Nowadays, the steganalysis of digital image mainly focuses on the design of steganalysis features to improve the universal blind detection (UBD) model's detection accuracy. However it has nothing to do with the testing images and is difficult to achieve high-precision detection. Based on large data training resources, this article studies the influence of steganography on image features to uncover the important relationship between steganalysis and image feature. Furthermore, the article proposes a steganalysis method for testing samples to select specialized training sets. The classical JPEG steganography algorithm nsF5 and the mainstream JPEG steganalysis features, such as CC-PEV, CC-Chen, CF^*, DCTR and GFR, are used as an example to organize the experiments. The results show that the accuracy of this method is higher than that of other similar methods.

Key words:information hiding;steganalysis;specific testing sample;high precision;machine learning

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张逸为,张卫明,俞能海.针对特定测试样本的隐写分析方法.软件学报,2018,29(4):987-1001

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Received:April 30,2017
Revised:June 26,2017
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Online: November 29,2017
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