Zhou Limengnan, Han Hongyu, Wu Hanzhou
School of Electronic and Information Engineering, Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China.
School of Computer Science, Sichuan Normal University, Chengdu 610000, China.
Entropy (Basel). 2021 Jul 19;23(7):917. doi: 10.3390/e23070917.
Reversible data hiding (RDH) has become a hot spot in recent years as it allows both the secret data and the raw host to be perfectly reconstructed, which is quite desirable in sensitive applications requiring no degradation of the host. A lot of RDH algorithms have been designed by a sophisticated empirical way. It is not easy to extend them to a general case, which, to a certain extent, may have limited their wide-range applicability. Therefore, it motivates us to revisit the conventional RDH algorithms and present a general framework of RDH in this paper. The proposed framework divides the system design of RDH at the data hider side into four important parts, i.e., binary-map generation, content prediction, content selection, and data embedding, so that the data hider can easily design and implement, as well as improve, an RDH system. For each part, we introduce content-adaptive techniques that can benefit the subsequent data-embedding procedure. We also analyze the relationships between these four parts and present different perspectives. In addition, we introduce a fast histogram shifting optimization (FastHiSO) algorithm for data embedding to keep the payload-distortion performance sufficient while reducing the computational complexity. Two RDH algorithms are presented to show the efficiency and applicability of the proposed framework. It is expected that the proposed framework can benefit the design of an RDH system, and the introduced techniques can be incorporated into the design of advanced RDH algorithms.
可逆数据隐藏（RDH）近年来已成为一个热点，因为它允许秘密数据和原始宿主都能被完美重建，这在要求宿主不退化的敏感应用中是非常理想的。许多RDH算法都是通过复杂的经验方法设计的。将它们扩展到一般情况并不容易，这在一定程度上可能限制了它们的广泛适用性。因此，这促使我们重新审视传统的RDH算法，并在本文中提出一个RDH的通用框架。所提出的框架将数据隐藏方的RDH系统设计分为四个重要部分，即二进制映射生成、内容预测、内容选择和数据嵌入，这样数据隐藏方就可以轻松地设计、实现并改进RDH系统。对于每个部分，我们都引入了可以使后续数据嵌入过程受益的内容自适应技术。我们还分析了这四个部分之间的关系，并给出了不同的观点。此外，我们引入了一种用于数据嵌入的快速直方图平移优化（FastHiSO）算法，以在降低计算复杂度的同时保持有效载荷-失真性能。提出了两种RDH算法来展示所提出框架的效率和适用性。期望所提出的框架能有利于RDH系统的设计，并且所引入的技术可以被纳入到先进RDH算法的设计中。
