General framework to histogram-shifting-based reversible data hiding

Area of Science:

• Computer Science
• Information Security
• Data Compression

Background:

• Reversible data hiding (RDH) is crucial for embedding information discreetly.
• Histogram shifting (HS) is an established RDH technique known for efficiency.
• Existing HS-based RDH methods offer high capacity and low distortion.

Purpose of the Study:

• To present a general framework for constructing histogram shifting-based reversible data hiding algorithms.
• To demonstrate the universality and applicability of the proposed framework.
• To introduce novel and efficient RDH algorithms derived from the framework.

Main Methods:

• Developed a general framework for HS-based RDH.
• Defined 'shifting' and 'embedding' functions as key components.
• Showcased existing RDH algorithms as special cases of the framework.
• Introduced two new HS-based RDH algorithms.

Main Results:

• The proposed framework unifies various HS-based RDH algorithms.
• New algorithms demonstrate the framework's effectiveness and efficiency.
• The framework allows for the design of RDH algorithms with high capacity and low distortion.

Conclusions:

• The general framework simplifies the development of HS-based RDH algorithms.
• The framework is versatile, encompassing existing and novel approaches.
• Future research can leverage this framework to devise more advanced RDH techniques.