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A Progressive Quality Hiding Strategy Based on Equivalence Partitions of Hiding Units

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Transactions on Data Hiding and Multimedia Security VI

Part of the book series: Lecture Notes in Computer Science ((TDHMS,volume 6730))

Abstract

Many sophisticated schemes are springing up recently with better characteristics, such as higher capacity and better security. However, if we tune the size of the secret message progressively, most methods do not provide the progressive quality characteristic which means that the relationship between the quality of stego image and the size of the secret message could be represented by a smooth curve without any jump points. This paper designs a novel hiding strategy based on an equivalence relation, which not only provides the progressive quality characteristic but also enhances remarkably the quality of stego image without sacrificing the security and capacity compared with original steganography schemes. In the proposed strategy, all hiding units can be partitioned into equivalence classes according to a constructed equivalence relation based on the capacity of hiding units. Following that, the hiding procedure is performed in predefined order in equivalence classes as the traditional steganography scheme. Because of considering the relation between the length of message and capacity, the hiding method using proposed hiding strategy outperforms the original approaches when embedding same message. Experimental results indicate that the proposed strategy gains up to 4.0 dB over existing hiding schemes.

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Liu, S., Yao, H., Zhang, S., Gao, W. (2011). A Progressive Quality Hiding Strategy Based on Equivalence Partitions of Hiding Units. In: Shi, Y.Q., et al. Transactions on Data Hiding and Multimedia Security VI. Lecture Notes in Computer Science, vol 6730. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24556-5_4

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  • DOI: https://doi.org/10.1007/978-3-642-24556-5_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24555-8

  • Online ISBN: 978-3-642-24556-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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