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논문검색

A Characteristic-Preserving Steganographic Method Based on Revision Identifiers

초록

영어

Since the majority of the available steganographic schemes in OOXML format documents suffered the disadvantages of unsatisfactory anti-detection capability and security level, a characteristic-preserving steganographic method with high security is proposed in this paper. The proposed method embeds secret information by replacing the last three bytes of the values of the revision identifiers in the main document body of the OOXML format document, while preserving the normal characteristics of the document. Meanwhile, position marks are added to track the locations of the embedded information. In order to keep the internal data consistency of the document, the newly created values are added into other related parts. Experimental results show that the method not only possesses good imperceptibility and anti-detection capability, but also has high security and large embedding capacity.

목차

Abstract
 1. Introduction
 2. Analysis of the Characteristics of the OOXML Format Document
  2.1. Analysis of the Data Characteristics in the Main Document Body
  2.2. Analysis of the Data Correlation Between Different Parts
  2.3. A Characteristic-Preserving Steganographic Method
 3. Experimental Results and Analysis
  3.1. Imperceptibility
  3.2. Anti-Detection Capability
  3.3 Embedding Capacity
  3.4. Security Analysis
 4. Conclusion
 Acknowledgements
 References

저자정보

  • Lingyun Xiang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha 410114, Hunan Province, China, School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, Hunan Province, China
  • Caixia Sun Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha 410114, Hunan Province, China, School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, Hunan Province, China
  • Niandong Liao Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha 410114, Hunan Province, China, School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, Hunan Province, China
  • Weizheng Wang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha 410114, Hunan Province, China, School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, Hunan Province, China

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