Faculty Publications
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Item An Improved and Secure Visual Secret Sharing (VSS) scheme for Medical Images(Institute of Electrical and Electronics Engineers Inc., 2019) Mhala, N.C.; Pais, A.R.Nowadays, medical information is being shared over the communication networks due to ease of technology. The patient's medical information has to be securely communicated over the network for automatic diagnosis. Most of the communication networks are prone to attacks from an intruder thus compromising the security of patients data. Therefore, there is a need to transmit medical images securely over the network. Visual secret sharing scheme can be used to transmit the medical images over the network securely. Visual Secret Sharing (VSS) scheme generates multiple shares to share secret information among n participants. To recover the secret information, all shares should be stacked together. In our previous work [9], we proposed a VSS based technique to recover secret images with the contrast of 70-80% known as Randomized Visual Secret Sharing (RVSS) scheme. However, RVSS scheme suffers from problems like 1) Generation of blocking artifacts in the recovered images. 2) It recovers medical images with a maximum contrast of 30-40%, hence it is not suitable for medical images.In this paper, we propose a modified RVSS scheme to recover the medical images with improved contrast. The proposed scheme introduces the idea of using super-resolution concept to improve the contrast of reconstructed medical images. The reconstruction quality of the medical images is evaluated using Human Visual System (HVS) based parameters. Additionally, the performance of the proposed system is evaluated using the existing Computer Aided Diagnosis (CAD) systems. The experimental results showed that the proposed system is able to reconstruct the secret image with the contrast of almost 85-90% and similarity of almost 77%. AIso, the reconstructed images using the proposed system achieves the similar classification accuracy as that of existing CAD systems. © 2019 IEEE.Item Randomised visual secret sharing scheme for grey-scale and colour images(Institution of Engineering and Technology journals@theiet.org, 2018) Mhala, N.C.; Jamal, R.; Pais, A.R.Randomised visual secret sharing is an encryption technique that utilises block-based progressive visual secret sharing and discrete cosine transform (DCT) based reversible data embedding technique to recover a secret image. The recovery method is based on progressive visual secret sharing, which recovers the secret image block by block. The existing block based schemes achieve the highest contrast level of 50% for noise-like and meaningful shares. The proposed scheme achieves a contrast level of 70-90% for noise-like and 70-80% for meaningful shares. The enhancement of contrast is achieved by embedding additional information in the shares using DCT-based reversible data embedding technique. Experimental results showed that the proposed scheme restores the secret image with better visual quality in terms of human visual system based parameters. © The Institution of Engineering and Technology 2017.Item A novel fingerprint template protection and fingerprint authentication scheme using visual secret sharing and super-resolution(Springer, 2021) Muhammed, A.; Mhala, N.C.; Pais, A.R.Fingerprint is the most recommended and extensively practicing biometric trait for personal authentication. Most of the fingerprint authentication systems trust minutiae as the characteristic for authentication. These characteristics are preserved as fingerprint templates in the database. However, it is observed that the databases are not secure and can be negotiated. Recent studies reveal that, if a person’s minutiae points are dripped, fingerprint can be restored from these points. Similarly, if the fingerprint records are lost, it is a permanent damage. There is no mechanism to replace the fingerprint as it is part of the human body. Hence there is a necessity to secure the fingerprint template in the database. In this paper, we introduce a novel fingerprint template protection and fingerprint authentication scheme using visual secret sharing and super-resolution. During enrollment, a secret fingerprint image is encrypted into n shares. Each share is stored in a distinct database. During authentication, the shares are collected from various databases. The original secret fingerprint image is restored using a multiple image super-resolution procedure. The experimental results show that the reconstructed fingerprints are similar to the original fingerprints. The proposed method is robust, secure, and efficient in terms of fingerprint template protection and authentication. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.Item GPGPU-based randomized visual secret sharing (GRVSS) for grayscale and colour images(Taylor and Francis Ltd., 2022) Holla, R.; Mhala, N.C.; Pais, A.R.Visual Secret Sharing (VSS) is a technique used for sharing secret images between users. The existing VSS schemes reconstruct the original secret image as a halftone image with only a 50% contrast. The Randomized Visual Secret Sharing (RVSS) scheme overcomes the disadvantages of existing VSS schemes. Although RVSS extracts the secret image with better contrast, it is computationally expensive. This paper proposes a General Purpose Graphics Processing Unit (GPGPU)-based Randomized Visual Secret Sharing (GRVSS) technique that leverages data parallelism in the RVSS pipeline. The performance of the GRVSS is compared with the RVSS in a generic and PARAM Shavak supercomputer architecture. The GRVSS outperforms the RVSS in both architectures. © 2020 Informa UK Limited, trading as Taylor & Francis Group.Item An Effective GPGPU Visual Secret Sharing by Contrast-Adaptive ConvNet Super-Resolution(Springer, 2022) Holla, M.R.; Pais, A.R.In this paper, we propose an effective secret image sharing model with super-resolution utilizing a Contrast-adaptive Convolution Neural Network (CCNN or CConvNet). The two stages of this model are the share generation and secret image reconstruction. The share generation step generates information embedded shadows (shares) equal to the number of participants. The activities involved in the share generation are to create a halftone image, create shadows, and transforming the image to the wavelet domain using Discrete Wavelet Transformation (DWT) to embed information into the shadows. The reconstruction stage is the inverse of the share generation supplemented with CCNN to improve the reconstructed image’s quality. This work is significant as it exploits the computational power of the General-Purpose Graphics Processing Unit (GPGPU) to perform the operations. The extensive use of memory optimization using GPGPU-constant memory in all the activities brings uniqueness and efficiency to the proposed model. The contrast-adaptive normalization between the CCNN layers in improving the quality during super-resolution impart novelty to our investigation. The objective quality assessment proved that the proposed model produces a high-quality reconstructed image with the SSIM of (89 - 99.8 %) for the noise-like shares and (71.6 - 90 %) for the meaningful shares. The proposed technique achieved a speedup of 800 × in comparison with the sequential model. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Item High-performance medical image secret sharing using super-resolution for CAD systems(Springer, 2022) Holla, M.R.; Pais, A.R.Visual Secret Sharing (VSS) is a field of Visual Cryptography (VC) in which the secret image (SI) is distributed to a certain number of participants in the form of different encrypted shares. The decryption then uses authorized shares in a pre-defined manner to obtain that secret information. Medical image secret sharing (MISS) is an emerging VSS field to address the performance challenges in sharing medical images, such as efficiency and effectiveness. Here, we propose a novel MISS for the histopathological medical images to achieve high performance in these two parameters. The novelty here is the Graphics Processing Unit (GPU) to exploit the data-parallelism in MISS during encryption and super-resolution (SR), supplementing effectiveness with efficiency. A Convolution Neural Network (CNN) for SR produces a high-contrast reconstructed image. We evaluate the presented model using standard objective assessment parameters and the Computer-Aided Diagnosis (CAD) systems. The result analysis confirmed the high-performance of the proposed MISS with a 98% SSIM of the deciphered image. Compared with the state-of-art deep learning models designed for the histopathological medical images, MISS outperformed with 99.71% accuracy. Also, we achieved a categorization precision that fits the CAD systems. We attained an overall speedup of 800 × over the sequential model. This speedup is significant compared to the speedups of the benchmark GPGPU-based medical image reconstruction models. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Item Secure latent fingerprint storage and self-recovered reconstruction using POB number system(Elsevier B.V., 2023) Muhammed, A.; Pais, A.R.Latent fingerprints are the unintentionally deposited fingerprint impressions gathered from the crime scenes. Many criminal investigation agencies consider latent fingerprints as a significant court accepted evidence. A typical latent fingerprint comes in low quality. Hence, a slight modification in the latent fingerprint may induce a marked shift in the recognition performance. Due to this, wrongdoers behind the crime scenes may try to remove or alter the latent fingerprint information by accessing the fingerprint database. Unlike regular fingerprint enrollment, retaking a latent fingerprint is not always possible. Preserving the latent fingerprints in a single database makes it vulnerable to single-point attacks. Hence, this paper presents a secure way to store and retrieve latent fingerprint information using POB-based (n,n) VSS technique. The proposed method encrypts each latent fingerprint as n secret shares, and stores them in n distinct databases. The distributed storage protects the data from single-point attacks. Along with secure storage, we also introduce a self-recovery mechanism in the case of fingerprint share tampering. The self-recovery mechanism protects the latent fingerprint from different tampering attacks. The proposed method has been evaluated using NIST Special Database4 (NIST-SD4) and IIIT Delhi latent fingerprint datasets. The experimental results show that the proposed technique offers secure distributed storage with lossless reconstruction of latent fingerprint images whenever needed. The proposed self-recovery mechanism enables the recovery of latent fingerprint images even in the case of share tampering. © 2023Item A secure fingerprint template generation mechanism using visual secret sharing with inverse halftoning(Academic Press Inc., 2023) Muhammed, A.; Pais, A.R.Fingerprints are the most popular and widely practiced biometric trait for human recognition and authentication. Due to the wide approval, reliable fingerprint template generation and secure saving of the generated templates are highly vital. Since fingers are permanently connected to the human body, loss of fingerprint data is irreversible. Cancelable fingerprint templates are used to overcome this problem. This paper introduces a novel cancelable fingerprint template generation mechanism using Visual Secret Sharing (VSS), data embedding, inverse halftoning, and super-resolution. During the fingerprint template generation, VSS shares with some hidden information are formulated as the secure cancelable template. Before authentication, the secret fingerprint image is reconstructed back from the VSS shares. The experimental results show that the proposed cancelable templates are simple, secure, and fulfill all the properties of the ideal cancelable templates, such as security, accuracy, non-invertibility, diversity, and revocability. The experimental analysis shows that the reconstructed fingerprint images are similar to the original fingerprints in terms of visual parameters and matching error rates. © 2023 Elsevier Inc.