Table 1 Summary of the studied works.
From: A new digital watermarking model using honey encryption and reversible cellular automata
References | Year | Research goal | Method | Limitations |
|---|---|---|---|---|
Mellimi et al.9 | 2021 | Develop a robust and efficient image watermarking scheme | LWT, DNN | High computational complexity |
Ding et al.10 | 2021 | Develop a generalized DNN-based watermarking technique | DNN-based approach | Susceptibility to JPEG compression attacks |
Fang et al.11 | 2023 | Develop a robust watermarking framework with high visual quality and accuracy | Invertible neural block for embedding and extraction | High computational complexity |
Dhaya12 | 2021 | Develop a lightweight CNN-based robust watermarking scheme | LW-CNN with integrated watermarking | High computational complexity |
Li et al.13 | 2021 | Develop a concealed attack methodology for robust watermarking | Generative adversarial networks, perceptual losses | Primarily focuses on attacking watermarking systems |
Wu et al.14 | 2020 | Develop a watermarking framework for DNNs to watermark outputs of various image processing tasks | Watermarking DNN outputs | Limited to specific image processing tasks |
Sharma and Mir15 | 2022 | Develop a time-efficient optimization technique for image watermarking | Machine learning algorithms (DCT, ACO, LGB) | Focuses on optimization and time efficiency, may not prioritize robustness or security |
Cao et al.16 | 2023 | Develop a screen-shooting resistant watermarking scheme | DNNs in the frequency domain | Primarily focuses on resistance to screen-shooting attacks |
Jamali et al.17 | 2023 | Develop an end-to-end network for robust and imperceptible watermarking | CNN with dynamic embedding and blind watermarking | High computational complexity |
Hao et al.20 | 2020 | Develop a robust image watermarking algorithm using GANs | GAN with generator, adversary module, and high-pass filter | High computational complexity |
Ge et al.19 | 2023 | Develop a robust document image watermarking scheme | DNNs with encoder, decoder, noise layer, and text-sensitive loss function | Primarily focused on document images |
Mahapatra et al.20 | 2023 | Develop a CNN-based watermarking algorithm with high invisibility and robustness | CNN-based embedding and extraction | High computational complexity |
Naffouti et al.21 | 2023 | Develop a robust and secure image watermarking system | SVD and DWT | Not explicitly mentioned in the provided text |
Zhao et al.22 | 2022 | Develop a robust image watermarking system using deep learning and attention mechanisms | DARI-mark framework with attention network | Computational expensive |
Zear and Singh23 | 2022 | Develop a secure and robust color image watermarking system | SVD, DCT, and Lifting wavelet transform | Not explicitly mentioned in the provided text |
Khudhair et al.24 | 2023 | Develop a secure and efficient reversible data hiding technique | Block-wise histogram shifting, LSB embedding, encryption | Primarily focuses on reversible data hiding, not general image watermarking |
Khaldi et al.25 | 2023 | Develop a secure medical image watermarking system for patient identification | Redundant DWT and Schur decomposition | Primarily focused on medical images |
Sayah et al.26 | 2024 | Develop a blind and high-capacity data hiding scheme for medical images | IWT and SVD | Primarily focused on medical images |
Hemalatha et al.27 | 2023 | Improve the performance of blind image steganalysis | Curvelet denoising, third-order SPAM features, ensemble classifier | Focuses on steganalysis, not watermarking |
