Faculty Publications
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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.Item Accelerating randomized image secret sharing with GPU: contrast enhancement and secure reconstruction using progressive and convolutional approaches(Springer, 2024) Holla, M.; Suma, D.; Pais, A.R.Image Secret Sharing (ISS) is a cryptographic technique used to distribute secret images among multiple users. However, current Visual Secret Sharing (VSS) schemes produce a halftone image with only 50% contrast when reconstructing the original image. To overcome this limitation, the Randomized Image Secret Sharing (RISS) scheme was introduced. RISS achieves a higher contrast of 70% when extracting the secret image but comes with a high computational cost. This research paper presents a novel approach called Graphics Processing Unit (GPU)-based Randomized Image Secret Sharing (GRISS), which utilizes data parallelism within the RISS pipeline. The proposed technique also incorporates an Autoencoder-based Single Image Super-Resolution (ASISR) to enhance the contrast of the recovered image. The performance of GRISS is evaluated against RISS, and the contrast of the ASISR images is compared to current benchmark models. The results demonstrate that GRISS outperforms state-of-the-art models in both efficiency and effectiveness. © The Author(s) 2024.