This approach was deemed secure due to its utilisation of encryption and confidential keys. [PERSON] [24] presented a reliable discrete cosine transform (DCT)-based watermark for blind grayscale images. The method under consideration employs mixed modulations and a partially sign-altered mean in the utilisation of the discrete cosine transform, which facilitates the attainment of data balance and enables the embedding of multiple coefficients, thereby enhancing attack strength while maintaining a low bit error rate. The proposed scheme demonstrates enhanced resilience and visual excellence with an average of 40 dB. Furthermore, [PERSON] et al. [33] presented a hybrid and resilient approach for embedding watermarks in colour images through the utilisation of Discrete Wavelet Transform (DWT) and LU decomposition. The proposed methodology involves an initial DWT-based transformation of the cover image. Subsequent to the initial stage, the Low-High (LH) and High-Low (HL) sub-bands are subjected to a process of selection and separation, resulting in the formation of discrete and mutually exclusive blocks. The blocks are subjected to LU decomposition, following which the encrypted watermark image is incorporated into the upper triangular matrices of the blocks. The plan under consideration exhibits a significant degree of resilience. Moreover, [PERSON] et al. [25] suggested using the Affine transformation and Schur decomposition to create a reliable blind colour image watermarking system. The upper triangular matrix of the Schur decomposition is used to quantify the diagonal eigenvalues after the watermark image has been encrypted using the Affine transformation. The suggested scheme has low computational requirements and good resistance to numerous attacks. [PERSON] and [PERSON] [26] presented a reliable DCT-based watermark for blind grayscale images. The proposed method utilises advanced techniques to enhance the security of the data and increase its strength against potential attacks. The proposed scheme offers excellent robustness and imperceptibility with a low bit error rate and an average of 40 dB. Another study investigated two distinct applications utilising deep learning-based generative adversarial networks (GANs) and transfer learning for magnetic resonance imaging (MRI) reconstruction procedures for brain and knee imaging. The approach facilitates the implementation of forthcoming MRI reconstruction models, obviating the need for extensive imaging datasets [34; 35; 36]. Additionally, [PERSON] et al. [37] introduced a robust watermarking technique for colour images that utilised DWT and DCT transformations. The watermark image is well protected with multiple security-enhancing techniques before embedding. The embedding process involves the selection of the middle-frequency band, followed by the application of \(8\times 8\) blocking and DCT. This is applied to the cover image's two-level DWT decomposition's middle-frequency band. The cover image is now ready to undergo the two-level DWT process after being shrunk. By determining the location of the watermark pixel for non-overlapping \(8\times 8\) blocks using column and row numbers, we can successfully embed the watermark. Furthermore, the watermark is securely embedded within the chosen blocks using a sophisticated low-frequency technique. [PERSON] and [PERSON] [27] proposed a novel watermarking scheme based on the wavelet transform and particle swarm optimization (PSO). In this scheme, the host image is transformed using wavelet transform, and the watermark is protected using a tent map and hash function. The scheme achieves high PSNR. [PERSON] and [PERSON] [21] used a variant of multi-objective-PSO to develop a watermarking scheme. The primary objective of this scheme is to select the leader with the shortest distance from the region that the particle has recently visited. [PERSON] and [PERSON] [28] proposed a kernel extreme learning machine and PSO-based watermarking scheme to secure watermarks.