Reversible Image Watermarking Using Modified Quadratic Difference Expansion and Hybrid Optimization Technique.

Authors

  • H. R. Lakshmi Institute of Technology, Visvesvaraya Technological University.
  • Surekha Borra Department of ECE, K. S. Institute of Technology.

DOI:

https://doi.org/10.9781/ijimai.2023.08.002

Keywords:

Search, Fractal Theory, Grey Wolf Optimization, Security, Watermarking

Abstract

With increasing copyright violation cases, watermarking of digital images is a very popular solution for securing online media content. Since some sensitive applications require image recovery after watermark extraction, reversible watermarking is widely preferred. This article introduces a Modified Quadratic Difference Expansion (MQDE) and fractal encryption-based reversible watermarking for securing the copyrights of images. First, fractal encryption is applied to watermarks using Tromino's L-shaped theorem to improve security. In addition, Cuckoo Search-Grey Wolf Optimization (CSGWO) is enforced on the cover image to optimize block allocation for inserting an encrypted watermark such that it greatly increases its invisibility. While the developed MQDE technique helps to improve coverage and visual quality, the novel data-driven distortion control unit ensures optimal performance. The suggested approach provides the highest level of protection when retrieving the secret image and original cover image without losing the essential information, apart from improving transparency and capacity without much tradeoff. The simulation results of this approach are superior to existing methods in terms of embedding capacity. With an average PSNR of 67 dB, the method shows good imperceptibility in comparison to other schemes.

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2025-06-01
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How to Cite

Lakshmi, H. R. and Borra, S. (2025). Reversible Image Watermarking Using Modified Quadratic Difference Expansion and Hybrid Optimization Technique. International Journal of Interactive Multimedia and Artificial Intelligence, 9(3), 141–154. https://doi.org/10.9781/ijimai.2023.08.002