Conference Papers

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Author: search.filters.author.Mhala, N.C.

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    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.
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    Secure transmission of Hyperspectral Images
    (Institute of Electrical and Electronics Engineers Inc., 2020) Srujana, O.S.; Mhala, N.C.; Pais, A.R.
    Hyperspectral Images (HSIs) are images that are captured across the electromagnetic spectrum. Hyperspectral images are represented using a three-dimensional (x,y,λ) data cube where dimension x and y represent the spatial dimension of a scene, and λ represents the spectral dimension of a scene. These images contain abundant information that has to be transmitted securely among the users for further processing. Visual Secret Sharing (VSS) is a modern cryptographic method used to send the visual data securely among n users. VSS scheme generates multiple shares of the secret image, and to recover secret image, these all shares need to be stacked together.In this paper, we propose a scheme for the secure transmission of hyperspectral images using VSS. We also introduce a band selection technique as a pre-processing step to reduce the redundancy and size of the image cube. The proposed scheme uses the super-resolution concept to increase the contrast of the resultant image, obtained from the VSS. We have performed the visual quality assessment of the reconstructed image using quantitative measurement parameters and we have compared the results with the existing VSS based Randomised VSS (RVSS) scheme. The experimental results showed that our proposed scheme achieves better reconstruction quality than the RVSS scheme and reconstructs HSI with a similarity of almost 76-95%. © 2020 IEEE.
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    An Illumination Invariant Approach to Salient Object Detection in Hyperspectral Images
    (Institute of Electrical and Electronics Engineers Inc., 2025) Lone, Z.A.; Pais, A.R.; Murali Krishna, M.M.; Mhala, N.C.
    Salient Object Detection (SOD) in Hyperspectral Images (HSI) has traditionally relied on low-level features, with limited exploration of high-level features. Low-level features lack semantic context, making them ineffective for detecting complex or subtle salient objects. Moreover, the inherent sensitivity of HSI to uneven illumination poses additional challenges, particularly when there is uneven illumination on the objects. To address these challenges, a novel SOD algorithm is proposed, that employs manifold ranking on high-level features learned through a deep segmentation network. Unlike existing methods that use raw HSI as input, the proposed approach utilizes spectral gradient as input, mitigating the effects of spectral variation. The learned features are ranked using a graph-based manifold ranking approach, which enhances the robustness of the detection process. Experimental evaluation on the HS-SOD dataset demonstrates that the proposed approach performs better, achieving superior results compared to state-of-the-art methods, with a precision of 0.63, an f1 score of 0.51, and a Mean Absolute Error (MAE) of 0.08. © 2025 IEEE.