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Experimental results of fingerprint (a) Image of the sample fingerprint, (b) Image after binarisation, (c) Minutiae points
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Information hiding is particularly used for security applications to protect the secret message from an unauthorised person. Due to the tremendous development of the Internet and its usage, the issue of protection over the internet is increasing. Under such a condition, transforming the information from the transmitter to the receiver requires more...
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The aim of this work is to introduce a new secure system for Medical Healthcare records. The proposed design influences cloud infrastructures to give cost competence, fast exploitation, scalability, and flexibility to have room for unstable workloads. The research system could be utilized to safeguard diverse medical health care records. The propos...
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... Comparing the robustness of our watermarking approach with recent similar works Thanki et al.[28] Zermi et al.[29] Alshanbari[30] Aparna and Kishore[31] Proposed ...
... This section compares the performance of the proposed scheme with similar approaches presented in (Ghouzali et al., 2021;Wadood et al., 2020;Aparna & Kishore 2019;Murillo-Escobar et al., 2016). Table 4 summarises the biometric modalities used in these approaches. ...
In the field of Electronic Health (e-Health), Electronic Health Records (EHR) are transmitted between health professionals using e-Health systems for cooperative medical practice, medical monitoring, telemedical expertise, and telemedical imaging. Medical images are a crucial component of EHR and are used in various aspects of telemedicine systems such as expertise, consultation, teaching, and research. However, protecting the authenticity and copyrights of medical images is essential to prevent duplication, modification, or unauthorized distribution. This paper proposes a robust medical image copyright protection method that uses patient palm-print template as watermark and Lorenz chaotic map for template concealing and selecting the appropriate embedding positions in medical images. The novelty of the method lies in optimizing the expected number of modifications per pixel of the medical images after being watermarked. Experimental results indicate that this approach has a high performance with a genuine accept rate of 99.86% and can withstand various image processing attacks, including Gaussian noise, compression, and image rotations, while ensuring personal data security during telemedicine data exchange.
... In many cases, the healthcare domain mandates pervasive approaches [15] in distinct interventions. Various health advances in research and technical aspects were discussed [16][17][18][19][20][21][22][23][24][25][26][27][28] using adaptable setups. ...
... 2023, 59, 153 3 of 13 [15] in distinct interventions. Various health advances in research and technical aspects were discussed [16][17][18][19][20][21][22][23][24][25][26][27][28] using adaptable setups. ...
... The image segmentation for natural images of flower algorithm FCTAC were proposed by Syed Inthiyaz et al, and PCA based fusion is applied texture extraction methods CLCM and CGT studied [14]. Puvvadi Aparna et al, proposed cryptography algorithm for E-healthcare application in medical image watermarking [15]. Image steganography technique novel n-rightmost bit replacement is proposed techniques with objectives to outperform SSIM, PSNR, EC, and resistance to Pepper noise, Salt noise and RS attack [16]. ...
... In this investigation, Aparna et al. in [28] detailed a medical image watermarking technique employing a variety of algorithms. The utilization of biometric fingerprint technology notably enhances the security of the watermarking system. ...
Securing medical imaging poses a significant challenge in preserving the confidentiality of healthcare data. Numerous research efforts have focused on fortifying these images, with encryption emerging as a primary solution for maintaining data integrity without compromising confidentiality. However, applying conventional encryption techniques directly to e-health data encounters hurdles, including limitations in data size, redundancy, and capacity, particularly in open-channel patient data transmissions. As a result, the unique characteristics of images, marked by their risk of data loss and the need for confidentiality, make preserving the privacy of data contents a complex task. This underscores the pressing need for innovative approaches to ensure the security and confidentiality of sensitive healthcare information within medical images. The proposed algorithm outperforms referenced algorithms in both image fidelity and steganographic capacity across diverse medical imaging modalities. It consistently achieves higher Peak Signal-to-Noise Ratio (PSNR) values, indicating superior image fidelity, reduced noise, and preserved signal quality in CT, MRI, ultrasound, and X-ray modalities. The experimental results demonstrate a considerable improvement in both the Peak Signal-to-Noise Ratio (PSNR) and maximum embedding capacity. Specifically, the average PSNR value for the X-ray modality reached a notable 73 dB, signifying superior image quality. Moreover, the CT modality exhibited the highest maximum embedding capacity, measured at 0.52, showcasing its efficiency in accommodating data within the images. Moreover, the algorithm consistently offers increased steganographic data hiding capacity in these images without perceptibly degrading their quality or integrity.
... Diabetes is a condition that occurs when our systems are unable to produce insulin hormones. According to the World Health Organization [1], diabetes was responsible for the death of 1.6 million in 2016. Diabetes patients have excessive blood glucose levels, which can cause organ failure and dysfunction. ...
Smart Healthcare which is based on deep learning is becoming increasingly popular because of its practical applications and has grown popular after its incorporation with IoT. Degenerative eye disease is the main factor of blindness in people of working age. Asian nations with large populations, like India and China, are on the edge of a diabetes epidemic. In terms of medical screening and diagnosis, a large number of diabetes patients posed a huge problem for skilled clinicians. The idea is to employ deep learning algorithms to detect blind spots in the eye and estimate the severity of the stage. We present an optimum approach for detecting blindness in retinal images based on recently released pre-trained EfficientNet models, as well as a comparative assessment of many innovative neural network models, in this study. On a benchmark dataset of retina images obtained by diagnostic imaging at various imaging phases, our EfficientNet-B5-based model assessment performs better than CNN and ResNet50 models.
... The main idea of the proposed MFMD scheme relied on the basic principle that has been adopted in medical images authentication schemes in which the medical image is divided into two regions called region-of-interest (ROI) and region-ofnon-interest (RONI) [55][56][57][58][59][60][61][62][63]. The ROI-based medical image authentication schemes focused their interest toward protecting the contents of the ROI because this region is important for the diagnosis process. ...
Face image manipulation detection (FIMD) is a research area of great interest, widely applicable in fields requiring data security and authentication. Existing FIMD schemes aim to identify manipulations in digital face images, but they possess individual strengths and limitations. Most schemes can only detect specific manipulations under certain conditions, leading to variable success rates across different images. The literature lacks emphasis on detecting manipulations involving multiple faces. This paper introduces a novel blind tamper detection and localization scheme specifically designed for multiple faces in digital images. The proposed multiple faces manipulation detection (MFMD) scheme consists of two stages: face detection and selection, and image watermarking. Through extensive experiments, the MFMD scheme's performance has been evaluated on various multiple‐face images, considering embedding capacity, payload, watermarked image quality, time complexity, and manipulation detection ability. The results demonstrate the MFMD scheme's efficacy in detecting different types of manipulations for multiple faces in images. Furthermore, the watermarked images exhibit high visual quality, even when multiple faces are present. The scheme's efficiency recommends it for practical applications, especially in sharing personal images over unsecured networks. This research advances FIMD techniques by addressing the neglected area of multiple‐face manipulation detection. With improved accuracy, faster processing times, and resilience against various manipulations, the MFMD scheme offers valuable capabilities for enhancing data security and authentication in real‐world scenarios.
... 3. From that shared secret key, generate an encryption key. 4. Utilizing that encryption key and symmetric encryption algorithm, decrypt the information. ...
... The key distribution algorithm is used to share a secret key, the encryption algorithm enables confidential communication, and the digital signature algorithm is used to authenticate the signer and validate the integrity of the message: GENERAL PROCEDURE OF ECC Both parties agree to some publicly-known data items The elliptic curve equation Each user generates their public/private key pair Private Key = an integer, x, selected from the interval [1, p-1]Public Key = product, Q, of private key and Generate public private Key pair using that curve, for both sender and receiver. 3. Generate a Shared secret key from the key pair.4. From that shared secret key, generate an encryption key. ...
Security is the most critical issue amid transmission of medical images because it contains sensitive information of patients. Medical image security is an essential method for secure the sensitive data when computerized images and their relevant patient data are transmitted across public networks. Sensitive images carry extensive important information and different features compared to standard images. Medical images have much more sensitive and essential information than any other digital image. Each pixel in the image can be necessary for the diagnosis process, and any deformation can result in a faulty diagnosis. The most robust securing of these images affects an image to the extent that it can be ignored; this is different from insensitive imagery as the border of redundancy is very low. The embedding capacity in medical images is deficient. Existing researchers present different data security techniques as cryptography and data hiding to guarantee data verification. But these approaches take more time and less security in medical image application. So in this project, implement Fragmented based Elliptical curve cryptography with Convolutional neural network algorithm to provide secure disease diagnosis system for medical images. Experimental results shows that the proposed system implemented Lung CT scan images that are collected from Open medical data sources and with high level security.
... Experimental results demonstrate that high PSNR values are maintained even under high noise (35db) attacks. P. Aparna et al. [9] assessed the trustworthiness, authentication, and confidentiality of medical imageries by evaluating the SHA-256, minutiae extraction, elliptical curve cryptographic, encoding algorithm, embedding and extraction processes. The proposed scheme demonstrated successful preservation of watermarked image, with 44 dB average PSNR value, and NC around 1 value. ...
... The proposed approach's performance under normal conditions can be evaluated based on the results obtained from comparing it with four existing approaches, namely P Garg et al. [11], B Mokashi et al. [10], Aparna et al. [9], and Alkhathami et al. [8], without any external interference. Table 6 SSIM & NC values with the related works [11], [10], [9], and [8]. ...
... The proposed approach's performance under normal conditions can be evaluated based on the results obtained from comparing it with four existing approaches, namely P Garg et al. [11], B Mokashi et al. [10], Aparna et al. [9], and Alkhathami et al. [8], without any external interference. Table 6 SSIM & NC values with the related works [11], [10], [9], and [8]. ...
Two-factor authentication (2FA) method provides an additional layer of security to the user accounts and systems beyond a single authentication factor like simple password. Now days biometric based authentication is widely adopted as it reduces impersonation fraud and account takeover attacks. Biometric data are relevant with the user’s personal information can potentially be exploited by the attacker in the future to compromise additional data of the user. Hence protection of the biometric data is also vital part along with the secure authentication of the protected data. In this proposed work, a 2FA mechanism is implemented using Spread Spectrum Watermarking method. Instead of storing fingerprint biometric data in the database, it is embedded in the physical token/security token as invisible watermarking with the user’s image. the user's unique ID (UID) is stored in the database and embedded as an invisible watermark in the physical or security token, which serves as the second factor. Once both authentication factors and other user information are embedded, it becomes a smart card. To access protected data, the user's fingerprint is compared to the embedded fingerprint on the smart card for identification. To further validate the user's identity, the embedded UID is decrypted and matched against the stored UID in the database. The proposed watermarking-based 2FA model's enhanced outcomes are demonstrated through a detailed simulation analysis. Comparative analysis of the results confirms the superiority of the proposed model over traditional biometric-based 2FA systems.
... In [37], a medical image with digital watermarking based on fingerprints has been proposed. In the proposed method, the hash of the region of interest (ROI) of the medical image, fingerprint, and patient's record (EHR) has been embedded into the medical image. ...
Secure transmission of medical images and medical data is essential in healthcare systems, both in telemedicine and AI approaches. The compromise of images and medical data could affect patient privacy and the accuracy of diagnosis. Digital watermarking embeds medical images into a non-significant image before transmission to ensure visual security. However, it is vulnerable to white-box attacks because the embedded medical image can be extracted by an attacker that knows the system’s operation and does not ensure the authenticity of image transmission. A visually secure image encryption scheme for secure fingerprint-based authenticated transmission has been proposed to solve the above issues. The proposed scheme embeds the encrypted medical image, the encrypted physician’s fingerprint, and the patient health record (EHR) into a non-significant image to ensure integrity, authenticity, and confidentiality during the medical image and medical data transmission. A chaotic encryption algorithm based on a permutation key has been used to encrypt the medical image and fingerprint feature vector. A hybrid asymmetric cryptography scheme based on Elliptic Curve Cryptography (ECC) and AES has been implemented to protect the permutation key. Simulations and comparative analysis show that the proposed scheme achieves higher visual security of the encrypted image and higher medical image reconstruction quality than other secure image encryption approaches.
