498 research outputs found
Efficiency of LSB steganography on medical information
The development of the medical field had led to the transformation of communication from paper information into the digital form. Medical information security had become a great concern as the medical field is moving towards the digital world and hence patient information, disease diagnosis and so on are all being stored in the digital image. Therefore, to improve the medical information security, securing of patient information and the increasing requirements for communication to be transferred between patients, client, medical practitioners, and sponsors is essential to be secured. The core aim of this research is to make available a complete knowledge about the research trends on LSB Steganography Technique, which are applied to securing medical information such as text, image, audio, video and graphics and also discuss the efficiency of the LSB technique. The survey findings show that LSB steganography technique is efficient in securing medical information from intruder
A medical image steganography method based on integer wavelet transform and overlapping edge detection
© Springer International Publishing Switzerland 2015. Recently, there has been an increased interest in the transmission of digital medical images for e-health services. However, existing implementations of this service do not pay much attention to the confidentiality and protection of patients’ information. In this paper, we present a new medical image steganography technique for protecting patients’ confidential information through the embedding of this information in the image itself while maintaining high quality of the image as well as high embedding capacity. This technique divides the cover image into two areas, the Region of Interest (ROI) and the Region of Non- Interest (RONI), by performing Otsu’s method and then encloses ROI pixels in a rectangular shape according to the binary pixel intensities. In order to improve the security, the Electronic Patient Records (EPR) is embedded in the high frequency sub-bands of the wavelet transform domain of the RONI pixels. An edge detection method is proposed using overlapping blocks to identify and classify the edge regions. Then, it embeds two secret bits into three coefficient bits by performing an XOR operation to minimize the difference between the cover and stego images. The experimental results indicate that the proposed method provides a good compromise between security, embedding capacity and visual quality of the stego images
Security System for Safe Transmission of Medical Images
This paper develops an optimised embedding of payload in medical images by using genetic optimisation. The goal is to preserve the region of interest from being distorted because of the watermark. By using this system there is no need to manually define the region of interest by experts as the system will apply the genetic optimisation to select the parts of image that can carry the watermark guaranteeing less distortion. The experimental results assure that genetic based optimisation is useful for performing steganography with less mean square error percentage
A Study of Data Security on E-Governance using Steganographic Optimization Algorithms
Steganography has been used massively in numerous fields to maintain the privacy and integrity of messages transferred via the internet. The need to secure the information has augmented with the increase in e-governance usage. The wide adoption of e-governance services also opens the doors to cybercriminals for fraudulent activities in cyberspace. To deal with these cybercrimes we need optimized and advanced steganographic techniques. Various advanced optimization techniques can be applied to steganography to obtain better results for the security of information. Various optimization techniques like particle swarm optimization and genetic algorithms with cryptography can be used to protect information for e-governance services. In this study, a comprehensive review of steganographic algorithms using optimization techniques is presented. A new perspective on using this technique to protect the information for e-governance is also presented. Deep Learning might be the area that can be used to automate the steganography process in combination with other method
A new efficient block matching data hiding method based on scanning order selection in medical images
Digital technology and the widespread use of the Internet has increased the speeds at which digital data can be obtained and shared in daily life. In parallel to this, there are important concerns regarding the confidentiality of private data during data transmissions and the possibility that data might fall into the hands of third parties. Issues relating to data safety can also affect patients' medical images and other information relating to these images. In this study, we propose a new method based on block matching that can be used to hide the patient information in medical images. In this method, 8 scanning orders (6 of which are newly designed) are developed to provide high image quality. By diversifying the number of scanning orders, we aim to achieve the lowest number of bit changes. The performance of the developed method is measured using the number of bits subject to change, the peak signal-to-noise ratio and the mean structural similarity index measure image quality assessment metrics, and steganalysis attacks. The method we developed was found to be more effective in hiding data compared to the classical least significant bit method.https://doi.org/10.3906/elk-1506-18
Lagrangian Recurrent Steganalysis and Hyper Elliptic Certificateless Signcryption for Secure Image Transmission
Present-day evolution in communication and information technology dispenses straightforward and effortless access to data, but the most noteworthy condition is the formation of secure communication. Numerous approaches were designed for safety communication. One of the crucial approaches is image steganography. Moreover, provisioning of information security services is arrived at via cryptosystems where cryptosystems make certain the secure messages transmission between the users in an untrustworthy circumstance. The conventional method of providing encryption and signature is said to be first signing and then encryption, but both the computation and communication costs are found to be high. A certificateless signcryption mechanism is designed to transfer the medical data or images securely. This mechanism will minimize the storage and verification costs of public key certificates. The author of this article proposes a method named Lagrangian recurrent Steganalysis and Hyper Elliptic Certificateless Signcryption for transferring the medical data or images securely. In two sections the LRS-HECS method is split. They are medical image steganalysis and certificateless signcryption. First with the Chest X-Ray images obtained as input, a Codeword Correlated Lagrangian Recurrent Neural Network-based image steganography model is applied to generate steg images. Second, to transfer the medical images securely the steg images provided as input is designed a model named a Hyper Elliptic Curve-based Certificateless Signcryption. The issue of providing the integrity and validity of the transmitted medical images and receiver anonymity is addressed by the application of Hyper Elliptic Curve. Chest X-Ray pictures were used in experimental simulations, and the findings showed that the LRS-HECS approach had more advantages over existing state-of-the-art methods in terms of higher peak signal to noise ratio with data integrity and with reduced encryption time and transmission cost
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