Table of ContentsSummaryIntroductionProblem AnalysisProposed MethodConclusionSummaryCommunication technology has advanced in recent years, increasing the requirements for secure data communication and information management. It is for this reason that many researchers have devoted much of their time and effort to finding suitable ways to hide important information. The proposed technique presents an effective storage security mechanism for protecting digital medical images by improving previous discrete wavelet transform (DWT) techniques. The quality of the stego image and the recovered image showed acceptable visual quality. The effectiveness of the proposed scheme will be proven by the well-known measure of imperceptibility of weighted peak signal-to-noise ratio (WPSNR), mean square error (MSE) and normalized cross-correlation coefficient (NCCC). The experimental results were compared with previous techniques. The working environment for the proposed system is MATLAB. Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not Be Banned”? Get an Original EssayIntroductionCopyright protection of digital multimedia like image, audio and video is very necessary with the development of networking and communication. In today's world, the cloud environment has become very popular and important to many people as a platform for storing and retrieving large data. To use it, it must be secure. Steganography is a way to maintain the confidentiality of transmitted information. Many methods such as cryptography, watermarking, fingerprinting, and encryption and decryption techniques have been advanced in order to secure information during communication. The calculation of the cryptography and the management of the keys limit its use and therefore the image recovered is of poor quality. Watermarking and fingerprinting mainly focus on preserving copyright ownership and have different algorithms. Both in the watermark and in the fingerprinting, the secret message hidden inside the holder can be visible. But in steganography, the fact that there is a secret message hidden in a file itself will be a secret. The most commonly used file format for communication is digital image due to its high frequency on the Internet. The image obtained after the masking process can be called stego image. Steganography is classified into two types: spatial domain and transformation domain. In spatial domain techniques, the secret data is directly hidden in the pixel intensity. The most popular and simplest steganographic technique for data hiding is least significant bit substitution (LSB). The process of integrating data in the transformation domain (frequency domain) is much more powerful than integration techniques that operate in the time domain. Today, most strong steganographic systems operate in the domain of transformations. Various applications have different requirements for the steganography method used. For example, some applications may require absolute invisibility of secret information, while other applications require a larger secret message to be hidden. The discrete wavelet transform (DWT) will be presented in detail and improvements will be applied to propose an algorithm to solve the long distance problem.processing time required for existing DWT techniques. The article is presented in five sections. Section I provides the introduction to this article. Section II discusses the problem analysis. Section III explains the proposed method. Section IV shows the implementation and results. Finally, Section V gives the conclusion of this paper.Problem AnalysisIn image-based steganography, it is necessary that the steganography technique is capable of hiding as many secret message bits as possible in an image in a manner which will not affect the two important requirements that are essential to the success of the obfuscation process: Security/imperceptibility: meaning that the human eye cannot distinguish between the original image and the stego image. Capacity: meaning the amount of secret data that can be accommodated in a cover media. The relationship between the two requirements should be balanced, in other words, if we increase the capacity beyond a certain limit, the imperceptibility will be affected and so on, so the digital steganography parameters should be chosen very thoroughly. Both DCT and DWT methods fall under transformation domain analysis and are the most common nowadays. Both methods have good imperceptibility and also robustness against statistical attacks. But as we know, the main objective of steganography is to increase robustness against attacks and also to increase payload capacity. In the case of capacity and processing time, DWT is good compared to DCT. DWT is very suitable for identifying the two important regions in images, region of interest (ROI) and region of non-interest (RONI). The region which contains the most important information/data, essentially the diagnostic part in the case of X-ray images, is called the ROI which has most of the energy of the image and called lower frequency sub-band ( LL). Thus, the integration of the secret message in sub-bands (LL) can considerably degrade the quality of the image. While the high frequency sub-bands (HH), (LH) and (HL) include the edges and textures of the image classified as (RONI) in which a secret message can be embedded effectively and where the human eye is less sensitive to changes in these sub-bands. This allows the secret message to be integrated without being perceived by the human eye, which is why most steganographic methods use RONI for data integration. Another problem is that during the process of embedding the secret image into the cover image, the embedding and extraction process time differs. with the secret size of the image. Time increases with size. This problem was solved using the proposed algorithm. Proposed Method The proposed technique depends on applying DWT in four steps instead of implementing it in one go. This saves processing time while the total integration time is less than the integration time of the one-step integration process. The proposed system of integration algorithm includes the following five modules. and the secret images into four equally sized subimages, each of which is a quarter of the size of the original image. In module 2, the DWT is applied to each of the cover and secret subimages (eight subimages). In module 3, each of the secret transformed subimages is integrated into the cover transformed subimages and the stego subimages (four subimages) result. In module 4, IDWT is applied to each of the stego subimages. In module 5, the final stego image is constructed by merging the four subimages into a single image. In the same way,.