# Secure NIfTI Image Authentication Scheme for Modern Healthcare System
## 2 Literature Review  
The astonishing improvements in teleraliology have gained the attention of researchers and motivated them to work on the problem of digital medical image authenticity. Essentially, medical pictures are heavily allocated to color and grayscale. However, most of the medical imaging modalities, such as CT scan, MRI, X-Ray, Ultrasound, and so on, typically provide grayscale images in various formats, such as.tiff,.png,.bmp, and.dcm. As a result, researchers are paying increasing attention to medical imaging because of its vast range of applications in the modern medical care framework. Several watermarking systems have been documented in the literature to retain the integrity and validity of these medical images. [PERSON] et al. [15] suggested a scheme using discrete Fourier transform and the DCT. They included a jumbled watermark in DCT's middle frequency. The authors also used ACM to encrypt the watermark. The authors tested this scheme with textured and natural images and compared it to other schemes. [PERSON] and [PERSON] [16] proposed a new method using Redundant Discrete Wavelet Transform (RDWT), Arnold Transform, and SVD. They applied RDWT, and then the LL sub-band was subjected to a homomorphic transform. Furthermore, the Arnold transform was used to scramble the watermark and insert it into the singular values of SVD. [PERSON] et al. [17] proposed a backpropagation neural network and an Arnold-transform-based scheme. A scrambled watermark bit was inserted into the output of the hidden layer. [PERSON] et al. [18] suggested a multiple-transformation-based scheme. To strengthen the resilience of this technique, they employed three well-known transforms: DWT, DCT, and discrete fractional random transform. They also used a logistic map and Arnold transform to encrypt the watermark prior to insertion. [PERSON] et al. offered a strategy for embedding a grey watermark into a grey image via a feature classification forest approach [19]. A watermarking approach based on the integer DCT domain was provided for inserting different grey watermarks within host grey images [20]. [PERSON] et al. developed a DWT domain-based technique by combining DCT and SVD and determining the watermark insertion strength using least-square curve fitting [21]. [PERSON] et al. [22] presented a system for grayscale image processing that utilized DCT and crosses inter-block prediction. Although copyright protection rules are increasingly strict, the grey NIfTI image digital watermarking approach has garnered little attention. [PERSON] et al. [23] investigated a hybrid approach to watermarking that combined DWT and DCT with SVD matrix decomposition. Additionally, they included the encrypted watermark by using the Arnold Cat Map. [PERSON] et al. [24] presented a watermarking approach using the Slantlet transform (SLT) and SVD. They began by dividing the entire image into ROI and NROI and selecting the NROI for watermark placement. NROI is further subdivided into \(8\times 8\) blocks, after which SLT is applied to these blocks, and SVD is applied to the LH sub-band. Finally, the bit of the watermark is embedded in the singular values. [PERSON] and [PERSON] [25] described a scheme that performs watermark embedding along with tamper detection and recovery to protect medical information. This scheme used SLT to embed data in ROI and RONI. The IWT coefficient was used to recover information from ROI. The method is resistant to various assaults. [PERSON] et al. [26] proposed a scheme using the Fast Walsh Transform (FWT), SLT, and SVD. In this scheme, they also used the NROI for watermark insertion. Fast Walsh Transform is applied on the NROI blocks, and then SVD is applied over these blocks to insert the first watermark, and the second watermark was inserted into the image's red channel using SLT transform. [PERSON] and [PERSON] [27] demonstrated a hybrid watermarking approach for grayscale images. This technique combines the Z and DWT transformations and the Bidiagonal SVD (BSVD) decomposition.