Vol.35, No.1, 2023, pp.537-552, doi:10.32604/iasc.2023.027070
OPEN ACCESS
ARTICLE
A Recursive High Payload Reversible Data Hiding Using Integer Wavelet and Arnold Transform
  • Amishi Mahesh Kapadia*, P. Nithyanandam
School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600012, Tamilnadu, India
* Corresponding Author: Amishi Mahesh Kapadia. Email:
Received 10 January 2022; Accepted 21 February 2022; Issue published 06 June 2022
Abstract
Reversible data hiding is an information hiding technique that requires the retrieval of the error free cover image after the extraction of the secret image. We suggested a technique in this research that uses a recursive embedding method to increase capacity substantially using the Integer wavelet transform and the Arnold transform. The notion of Integer wavelet transforms is to ensure that all coefficients of the cover images are used during embedding with an increase in payload. By scrambling the cover image, Arnold transform adds security to the information that gets embedded and also allows embedding more information in each iteration. The hybrid combination of Integer wavelet transform and Arnold transform results to build a more efficient and secure system. The proposed method employs a set of keys to ensure that information cannot be decoded by an attacker. The experimental results show that it aids in the development of a more secure storage system and withstand few tampering attacks The suggested technique is tested on many image formats, including medical images. Various performance metrics proves that the retrieved cover image and hidden image are both intact. This System is proven to withstand rotation attack as well.
Keywords
Reversible data hiding (RDH); integer wavelet transforms (IWT); arnold transform; payload; embedding and extraction
Cite This Article
A. Mahesh Kapadia and P. Nithyanandam, "A recursive high payload reversible data hiding using integer wavelet and arnold transform," Intelligent Automation & Soft Computing, vol. 35, no.1, pp. 537–552, 2023.
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