# A Novel Fragmented Approach for Securing Medical Health Records in Multimodal Medical Images
## 2 Recent Studies  
In ref. [3], the authors present an image steganography technique based on framelet transform to hide secret images in the cover image. Application of various steganalysis techniques showed poor detection accuracy, while visual properties and quality were better and more robust. In [4], the authors presented a review of both steganography and cryptography techniques and proposed a combined hybrid system that utilized the strengths of both techniques. The authors conclude that a hybrid system consisting of both concealed cryptographic messages using steganography will be more secure than using each one individually. In ref. [5], the authors propose a cloud-based reversible dynamic steganography technique for embedding pathological reports into medical images. The technique is based on dividing the image into five sub-regions, and a secret key generator is used to randomly select one of the sub-regions for hiding the pathological reports. The key is communicated to the receiving end through other means so that it can be used to extract pathological reports from the images. The quality of the image was intact, which was also shown through the analysis of the mean square error (MSE) and peak signal-to-noise ratio (PSNR) metrics. In ref. [6], the authors proposed a new way for steganography to be applied to medical images using LSB. This technique is based on the Local Binary Pattern (LBP). They divided the cover images into several non-overlapping patterns. The LBP operator is utilized to identify pixel embedding positions, while the LBP values are employed to determine the method and location for embedding the secret image pixel. The LSB method is used to embed the secret message within the cover image pixels. The method was evaluated using MSE and PSNR, demonstrating improved efficiency. In ref. [7], the author proposed a method that utilizes deep neural networks to hide and reveal hidden information via steganography. The method does not use LSB but rather compresses and distributes the secret data (image) representation across all of the available bit pixels. In ref. [8], the authors propose a coverless image steganography method using scale invariant transform and a set of features. The authors claim that their method transmits natural images that have the concealed information and yet have the same features as the secret data to the receiver. In their method, the secret data are converted into a bitstream. An inverted index is used to send the images with concealed data. Their proposed method resists steganalysis tools since the original image is never modified. In ref. [9], the authors proposed an image steganography technique for images sent over social media. They take the channel-compressed version of the image and conceal data in the compressed image using any JPEG stenographic method, thus producing the stego image. The coefficient adjustment method is then applied to the stego image for minor modification while still maintaining the image to stay the same as the stego image. On the receiving end, secret data are extracted with 100% accuracy. In ref. [10], the authors proposed a method based on generative adversarial networks for generating secure and suitable covers for steganography. One generative network and two discriminative networks are used in the proposed method. The generative networks check the visual quality, while the discriminative networks check the technical suitability of the images for data hiding. Improved convergence speed, image quality, and training stability are reported. The method was tested and found to be effective and robust. In [11], the authors propose a steganographic technique based on object detection and relationship mapping using coverless data hiding and steganography. Using object detection, the object color, type, and secret messages are mapped, and suitable locations are identified to hide the secret messages. In ref. [12], the authors proposed a medical JPEG image steganography method using dependencies of interblock coefficients.