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An efficient and secured blind image watermarking using ABC optimization in DWT and DCT domain

  • 1211: AIoT Support and Applications with Multimedia
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A Correction to this article was published on 16 June 2022

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Abstract

In today's digital world, all content is available over the internet, which anyone can use. Securing this data by adding copyright protection or ownership is called watermarking. This paper proposes a blind watermarking technique based on frequency domain transform to provide robustness and imperceptibility, and it also uses Artificial Bee Colony (ABC) optimization technique for optimization purposes. The ABC algorithm helps in finding the best embedding factor used during the embedding of the watermark. During extraction, only the watermarked image is needed that makes this technique blind watermarking. In this paper, a hybrid method using the logic of 2-level Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) is used for embedding and for providing security to the scheme, an encrypted image is embedded in the cover image as the watermark. The proposed technique is applied to several images to prove its robustness and imperceptibility. The proposed scheme is implemented on various images, and its performance is measured by performing several attacks to compare with other existing methods. The experimental results show that Peak Signal to Noise Ratio (PSNR) value greater than 40 is achieved here. Furthermore, normalized Correlation (NC) value is greater than 0.9, proving its robustness against various attacks.

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Authors and Affiliations

  1. University School of Information Communication and Technology, Guru Gobind Singh Indraprastha University, Dwarka, New Delhi, India

    Preeti Garg & R. Rama Kishore

  2. KIET Group of Institutions Delhi-NCR, Ghaziabad, India

    Preeti Garg

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  1. Preeti Garg
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  2. R. Rama Kishore
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Correspondence to Preeti Garg.

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Garg, P., Kishore, R.R. An efficient and secured blind image watermarking using ABC optimization in DWT and DCT domain. Multimed Tools Appl 81, 36947–36964 (2022). https://doi.org/10.1007/s11042-021-11237-9

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  • DOI: https://doi.org/10.1007/s11042-021-11237-9

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