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Sine cosine bird swarm algorithm-based deep convolution neural network for reversible medical video watermarking

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Abstract

Recently, advancements in multimedia have made a huge impact on watermarking technologies. The digital video watermarking is the process of embedding the data in the video. One of the major concerns in digital video watermarking is maintaining the quality of video besides preserving the privacy of the data. The aim of the research is to develop a reversible medical video watermarking using Sine Cosine Bird Swarm Algorithm-based Deep Convolutional Neural Network (SCBSA-based Deep CNN) for embedding the secret message in video frames. The development methodology is explained as follows. The SCBSA is developed by integrating Sine Cosine Algorithm (SCA) with the Bird Swarm Algorithm (BSA). The key frames are extracted from the input video using Minkowski distance and Wavelet distance. The features, like Neighborhood-based features, Convolutional Neural Network (CNN) features, Local Optimal Oriented Pattern (LOOP), and histogram features are obtained from the key frames. The interesting region is identified using DCNN, which is trained using the developed SCBSA. The secret message is embedded in the video in the embedding phase, whereas the embedded secret message is extracted in the extraction phase. The embedding and extracting process are carried out through two level decompositions using wavelet transform and inverse wavelet transform. The developed SCBSA-based Deep CNN uses the metrics, such as correlation coefficient, Mean Square Error and Peak signal-to-noise ratio (PSNR) for evaluating the performance. The developed SCBSA method is evaluated using the Mean Square Error, Correlation Coefficient and PSNR with Gaussian noise, Impulse noise, Salt and Pepper noise and in the absence of noise. The developed SCBSA method obtained a maximum correlation coefficient of 0.8990, minimum Mean Square Error of 0.0215 and maximum PSNR of 35.19, respectively while comparing with the existing reversible medical video watermarking methods.

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Data availability

The datasets analyzed during the current study are available on link https://nearlab.polimi.it/medical/dataset/ or from the corresponding author on reasonable request.

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Authors and Affiliations

  1. Department of Electronics & Instrumentation Engineering, JSS Academy of Technical Education (Visvesvaraya Technological University), Srinivaspura, 560 060, Bengaluru, India

    Subodh S. Ingaleshwar & D. Jayadevappa

  2. Department of Computer Science & Engineering, Rajarambapu Institute of Technology, Shivaji University, Sakharale, 415414, Maharashtra, India

    Nagaraj V. Dharwadkar

Authors
  1. Subodh S. Ingaleshwar
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  2. D. Jayadevappa
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  3. Nagaraj V. Dharwadkar
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Correspondence to Subodh S. Ingaleshwar.

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Ingaleshwar, S.S., Jayadevappa, D. & Dharwadkar, N.V. Sine cosine bird swarm algorithm-based deep convolution neural network for reversible medical video watermarking. Multimed Tools Appl 82, 36687–36712 (2023). https://doi.org/10.1007/s11042-023-14495-x

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