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An Optimized Deep Fusion Convolutional Neural Network-Based Digital Color Image Watermarking Scheme for Copyright Protection

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Abstract

The active use of the Internet and multimedia content has recently escalated copyright violations. Digital content, especially images and videos are subject to vulnerable attacks. It is also possible that an attacker might remove the watermark from the original image. Therefore, the copyright of digital images must be secured to prevent them from being inappropriately misused. This paper proposes an Enhanced Chimp Optimization algorithm based on Deep Fusion Convolutional Neural Network (ECO-DFCNN) for robust watermarking. The proposed framework consists of an embedding and extraction network to embed and extract the watermark. The octave convolutional model introduced in the embedding network captures various features and decreases spatial redundancy. In addition, the ECO algorithm is introduced to overcome the trade-off between robustness and imperceptibility by determining the optimal strength factor. The pyramid feature extraction module in the extraction network extracts the local features and the dilated convolutions minimize the model parameters. The proposed ECO-DFCNN method is tested against various attacks such as histogram equalization, compression, cropping, scaling, blurring, and median filtering. The proposed ECO-DFCNN method is evaluated and the performance is determined by comparing the obtained results with the existing watermarking techniques. The results show that the proposed ECO-DFCNN watermarking method is robust against various attacks while maintaining excellent imperceptibility with a high Peak Signal-to-Noise Ratio of 54.64 dB, Normalized Correlation of 0.98 and Structural Similarity Index Measure of 0.97, and low Bit Error Rate of 0.038.

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Authors and Affiliations

  1. Department of CSE, Rgpv University, Bhopal, MP, 462023, India

    Manish Rai & Sachin Goyal

  2. Department of IT, Rgpv University, Bhopal, MP, 462023, India

    Mahesh Pawar

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  1. Manish Rai
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All the authors have participated in writing the manuscript and have revised the final version. All authors read and approved the final manuscript.

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Correspondence to Manish Rai.

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Data Availability Statement

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Manish Rai, Sachin Goyal, Mahesh Pawar. The first draft of the manuscript was written by Manish Rai and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Conceptualization: Manish Rai; Methodology: Manish Rai; Formal analysis and investigation: Manish Rai, Sachin Goyal; Writing—original draft preparation: Manish Rai, Mahesh Pawar; Writing—review and editing: Sachin Goyal; Supervision: Mahesh Pawar

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Rai, M., Goyal, S. & Pawar, M. An Optimized Deep Fusion Convolutional Neural Network-Based Digital Color Image Watermarking Scheme for Copyright Protection. Circuits Syst Signal Process 42, 4019–4050 (2023). https://doi.org/10.1007/s00034-023-02299-1

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