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arXiv 1801.04747cs.MM
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Ensemble可逆数据隐藏

Ensemble Reversible Data Hiding

Hanzhou Wu, Wei Wang, Jing Dong, Hongxia Wang

摘要:

传统的可逆数据隐藏(RDH)算法通常将主机作为一个整体来嵌入秘密载荷。为了达到满意的率失真性能,秘密比特被嵌入到主机的噪声类分量,如预测误差。从率失真优化的角度来看,它可能不是最佳的,因为数据嵌入单元使用相同的参数。这促使我们提出了一个分段的数据嵌入策略,在本文中,有效的RDH,其中的原始主机可以被划分为多个子主机,使每个可以自由地优化和使用的数据嵌入参数。此外,它使我们能够应用不同的RDH算法在不同的子主机,这被定义为集成。请注意,这里定义的集成与机器学习中的集成不同。因此,常规操作对应于所提出的工作的特殊情况。由于这是一个通用的策略,我们结合一些国家的最先进的算法,构建一个新的系统,使用所提出的嵌入策略来评估率失真性能。实验结果表明,在大多数情况下,集成RDH系统的性能优于原始版本,显示了其优越性和适用性。

英文摘要:

The conventional reversible data hiding (RDH) algorithms often consider the host as a whole to embed a secret payload. In order to achieve satisfactory rate-distortion performance, the secret bits are embedded into the noise-like component of the host such as prediction errors. From the rate-distortion optimization view, it may be not optimal since the data embedding units use the identical parameters. This motivates us to present a segmented data embedding strategy for efficient RDH in this paper, in which the raw host could be partitioned into multiple subhosts such that each one can freely optimize and use the data embedding parameters. Moreover, it enables us to apply different RDH algorithms within different subhosts, which is defined as ensemble. Notice that, the ensemble defined here is different from that in machine learning. Accordingly, the conventional operation corresponds to a special case of the proposed work. Since it is a general strategy, we combine some state-of-the-art algorithms to construct a new system using the proposed embedding strategy to evaluate the rate-distortion performance. Experimental results have shown that, the ensemble RDH system could outperform the original versions in most cases, which has shown the superiority and applicability.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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