Earticle

초록

영어

Scrambling transformation plays an important role in information hiding application, so offering an effective evaluation method for scrambling algorithms is becoming increasingly necessary. The paper firstly analyzed the Arnold transformation process to get some universal rules about the periodicity of scrambling process, then used the improved Intersecting Cortical Model Neural Network (ICMNN) designed especially to extract 1D signatures of the original image and scrambled images which could effectively reflect the image structure changing processing. Finally L1 norm was adopted to evaluate the scrambling degree and the universal rules obtained above were used to verify the results. The experimental results showed that the proposed method could analyze and evaluate the scrambling degree efficiently and had a promising application future.

목차

Abstract
 1. Introduction
 2. The Scrambling Algorithm based on Arnold Transformation
 3. Improved Intersecting Cortical Model Neural Network (ICMNN)
 4. Image Signature Extraction and Scrambling Degree Evaluation
 5. Experimental Simulation and Analysis
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Chunlin Li [ College of Computer and Information Technology, China Three Gorges University ]
• Guangzhu Xu [ College of Computer and Information Technology, China Three Gorges University ] Corresponding author
• Chunxian Song [ College of Computer and Information Technology, China Three Gorges University ]
• Jing Jing [ College of Computer and Information Technology, China Three Gorges University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605