Protection of bio medical iris image using watermarking and cryptography with WPT

Mothi, R.; Karthikeyan, Marappan

doi:10.1016/j.measurement.2018.12.030

Cited by 25 publications

(7 citation statements)

References 8 publications

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“…Biometric security encompasses much more than just facial recognition. Hand geometry, eye scans, and lip-reading are just a few of the additional ways of identifying a person that has been developed [25].…”

Section: Lip-biometricmentioning

confidence: 99%

Novel Multimodal Biometric Feature Extraction for Precise Human Identification

Vasavi¹,

Abirami²

2023

Intelligent Automation &Amp; Soft Computing

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In recent years, biometric sensors are applicable for identifying important individual information and accessing the control using various identifiers by including the characteristics like a fingerprint, palm print, iris recognition, and so on. However, the precise identification of human features is still physically challenging in humans during their lifetime resulting in a variance in their appearance or features. In response to these challenges, a novel Multimodal Biometric Feature Extraction (MBFE) model is proposed to extract the features from the noisy sensor data using a modified Ranking-based Deep Convolution Neural Network (RDCNN). The proposed MBFE model enables the feature extraction from different biometric images that includes iris, palm print, and lip, where the images are preprocessed initially for further processing. The extracted features are validated after optimal extraction by the RDCNN by splitting the datasets to train the feature extraction model and then testing the model with different sets of input images. The simulation is performed in matlab to test the efficacy of the modal over multi-modal datasets and the simulation result shows that the proposed method achieves increased accuracy, precision, recall, and F1 score than the existing deep learning feature extraction methods. The performance improvement of the MBFE Algorithm technique in terms of accuracy, precision, recall, and F1 score is attained by 0.126%, 0.152%, 0.184%, and 0.38% with existing Back Propagation Neural Network (BPNN), Human Identification Using Wavelet Transform (HIUWT), Segmentation Methodology for Non-cooperative Recognition (SMNR), Daugman Iris Localization Algorithm (DILA) feature extraction techniques respectively.

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Section: Lip-biometricmentioning

confidence: 99%

Novel Multimodal Biometric Feature Extraction for Precise Human Identification

Vasavi¹,

Abirami²

2023

Intelligent Automation &Amp; Soft Computing

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“…This has led the attention of various researchers towards enhancing the security of medical images. Various techniques have been developed for the secure communication of medical images along with the data being hidden within these images [ 19 , 25 , 49 ]. The main aim is that no person other than the two parties should be able to extricate the confidential message from the image.…”

Section: Introductionmentioning

confidence: 99%

“…The main aim is that no person other than the two parties should be able to extricate the confidential message from the image. For such a purpose various data-hiding approaches like steganography, cryptography, fingerprinting, and digital watermarking have been proposed [ 8 , 19 , 20 , 25 , 49 , 93 ]. The pros and cons of these methods have been presented in Table 1 .…”

Section: Introductionmentioning

confidence: 99%

Advances in medical image watermarking: a state of the art review

Gull

Parah

2023

Multimed Tools Appl

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Watermarking has been considered to be a potent and persuasive gizmo for its application in healthcare setups that work online, especially in the current COVID-19 scenario. The security and protection of medical image data from various manipulations that take place over the internet is a topic of concern that needs to be addressed. A detailed review of security and privacy protection using watermarking has been presented in this paper. Watermarking of medical images helps in the protection of image content, authentication of Electronic Patient Record (EPR), and integrity verification. At first, we discuss the various prerequisites of medical image watermarking systems, followed by the classification of Medical Image Watermarking Techniques (MIWT) that include state-of-the-art. We have classified MIWT’s into four broader classes for providing better understanding of medical image watermarking. The existing schemes have been presented along with their cons so that the reader may be able to grasp the shortcomings of the technique in order to develop novel techniques proving the inevitability of the presented review. Further, various evaluation parameters along with potential challenges pertaining to medical image watermarking systems have been discussed to provide a deep insight into this research area.

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“…In the case of medical images, legitimacy is another important issue [8]. Therefore, it is important to put a lot of emphasis on legitimacy and integrity when sharing medical images [9][10][11]. Steganography [12], data encryption [13], and digital watermarking [14] are some examples of useful techniques to secure digital content.…”

Section: Introductionmentioning

confidence: 99%

Robust Medical Image Watermarking Scheme Using PSO, LWT, and Hessenberg Decomposition

et al. 2023

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Digital imaging is a technology that is extensively employed in diverse diagnostic examinations such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound imaging, among other modalities. Transferring a patient’s diagnostic images and medical data to a specialist physician in a distinct geographical location is conducted to facilitate an accurate diagnosis. The safeguarding of patient data privacy and confidentiality is ensured through the utilisation of smart hospital applications for medical data security. The current research presents the effective utilisation of lifting wavelet transform (LWT) and Hessenberg-based particle swarm optimization in order to generate resilient and safeguarded watermarks on ultrasound images. The empirical evidence suggests that our innovative approach outperforms our prior methodology, established through extensive testing. The watermark’s imperceptibility and accuracy are exemplified by its capacity to sustain a superior structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR), even amidst diverse image processing assaults.

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Protection of bio medical iris image using watermarking and cryptography with WPT

Cited by 25 publications

References 8 publications

Novel Multimodal Biometric Feature Extraction for Precise Human Identification

Novel Multimodal Biometric Feature Extraction for Precise Human Identification

Advances in medical image watermarking: a state of the art review

Robust Medical Image Watermarking Scheme Using PSO, LWT, and Hessenberg Decomposition

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