Abstract
Colour images have been widely used in many aspects of life; however, copyright violation issues related to these images motivate research efforts. This paper aims to develop an enhanced watermarking algorithm for producing a watermarked image using hybrid optimisation with high imperceptibility and robustness. The algorithm is based on spatial and transform domains and begins by embedding multiple secret marks into cover media using an optimal scaling factor. The multi-type mark contributes an additional level of authenticity to the proposed algorithm. Furthermore, the marked image is encrypted using an improved encryption scheme, and the denoising convolutional neural network (DnCNN) is employed to enhance the robustness of the proposed algorithm. The results reveal that the proposed watermarking algorithm yields low computational overhead, excellent watermark capacity, imperceptibility, and robustness to common filtering attacks. Moreover, the comparison shows that the proposed algorithm outperforms other competing methods.
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Acknowledgements
This work is supported by research project entitled “Copyright protection tool for digital data (DLRL/21CR0003/SWCC&ENT/GN/LP) dt. 29 August, 2020, DLRL, Hyderabad, India.
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Both authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by both authors. The first draft of the manuscript was written by DKM and AKS commented on previous versions of the manuscript. Both authors read and approved the final manuscript.
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Communicated by Irfan Uddin.
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Mahto, D.K., Anand, A. & Singh, A.K. Hybrid optimisation-based robust watermarking using denoising convolutional neural network. Soft Comput 26, 8105–8116 (2022). https://doi.org/10.1007/s00500-022-07155-z
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DOI: https://doi.org/10.1007/s00500-022-07155-z