Detection and localization for watermarking technique using LSB encryption for DICOM Image

Gupta, Mukesh Kumar; Dadheech, Pankaj; Kumar, Ankit; Dogiwal, Sanwta Ram; Poonia, Ramesh Chandra; Raja, Linesh; Bhatt, Devershi Pallavi

doi:10.1080/09720529.2021.2009193

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…The SARS-CoV-2 disease can be identified with different human-generated data, such as X-ray, CTscan, RTPCR, patient tweets, and respiratory sounds. Medical researchers have implemented several deep learning (DL), machine learning (ML), and signal processing (SP) models to diagnose SARS-CoV-2 with the human pulmonary sounds (cough, breathing, and voice) and images (X-ray and CT-scan) [17][18][19][20][21][22][23]. Therefore, there are numerous options available to identify SARS-CoV-2, and one of the approaches is human respiratory sound auscultations (HRSA).…”

Section: Introductionmentioning

confidence: 99%

COVID-19: respiratory disease diagnosis with regularized deep convolutional neural network using human respiratory sounds

Kumar

Alphonse

2022

Eur. Phys. J. Spec. Top.

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Human respiratory sound auscultation (HRSA) parameters have been the real choice for detecting human respiratory diseases in the last few years. It is a challenging task to extract the respiratory sound features from the breath, voice, and cough sounds. The existing methods failed to extract the sound features to diagnose respiratory diseases. We proposed and evaluated a new regularized deep convolutional neural network (RDCNN) architecture to accept COVID-19 sound data and essential sound features. The proposed architecture is trained with the COVID-19 sound data sets and gives a better learning curve than any other state-of-the-art model. We examine the performance of RDCNN with Max-Pooling (Model-1) and without Max-Pooling (Model-2) functions. In this work, we observed that RDCNN model performance with three sound feature extraction methods [Soft-Mel frequency channel, Log-Mel frequency spectrum, and Modified Mel-frequency Cepstral Coefficient (MMFCC) spectrum] for COVID-19 sound data sets (KDD-data, ComParE2021-CCS-CSS-Data, and NeurlPs2021-data). To amplify the models’ performance, we applied the augmentation technique along with regularization. We have also carried out this work to estimate the mutation of SARS-CoV-2 in the five waves using prognostic models (fractal-based). The proposed model achieves state-of-the-art performance on the COVID-19 sound data set to identify COVID-19 disease symptoms. The model’s learnable parameter gradients have vanished in the intermediate layers while optimizing the prediction error which is addressed with our proposed RDCNN model. Our experiments suggested that 3 × 3 kernel size for regularized deep CNN (without max-pooling) shows 2–3% better classification accuracy compared to RDCNN with max-pooling. The experimental results suggest that this new approach may achieve the finest results on respiratory diseases.

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Section: Introductionmentioning

confidence: 99%

COVID-19: respiratory disease diagnosis with regularized deep convolutional neural network using human respiratory sounds

Kumar

Alphonse

2022

Eur. Phys. J. Spec. Top.

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Secure NIfTI Image Authentication Scheme for Modern Healthcare System

et al. 2023

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Advances in digital neuroimaging technologies, i.e., MRI and CT scan technology, have radically changed illness diagnosis in the global healthcare system. Digital imaging technologies produce NIfTI images after scanning the patient’s body. COVID-19 spared on a worldwide effort to detect the lung infection. CT scans have been performed on billions of COVID-19 patients in recent years, resulting in a massive amount of NIfTI images being produced and communicated over the internet for diagnosis. The dissemination of these medical photographs over the internet has resulted in a significant problem for the healthcare system to maintain its integrity, protect its intellectual property rights, and address other ethical considerations. Another significant issue is how radiologists recognize tempered medical images, sometimes leading to the wrong diagnosis. Thus, the healthcare system requires a robust and reliable watermarking method for these images. Several image watermarking approaches for .jpg, .dcm, .png, .bmp, and other image formats have been developed, but no substantial contribution to NIfTI images (.nii format) has been made. This research suggests a hybrid watermarking method for NIfTI images that employs Slantlet Transform (SLT), Lifting Wavelet Transform (LWT), and Arnold Cat Map. The suggested technique performed well against various attacks. Compared to earlier approaches, the results show that this method is more robust and invisible.

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A DWT-Based Approach with Gradient Analysis for Robust and Blind Medical Image Watermarking

Hebbache,

Khaldi,

Aiadi

et al. 2024

Applied Sciences

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The growing adoption of telemedicine necessitates robust security measures for medical images during transmission. This paper proposes a novel blind watermarking system for medical images that utilizes both image gradients and the Discrete Wavelet Transform (DWT). Image gradients, acting as spatial derivatives, provide a “topological map” of the image, aiding in the identification of areas susceptible to disruption. The DWT, with its multi-resolution analysis, offers a favorable balance between robustness and imperceptibility. The proposed method embeds the watermark within the low–low band (LL) of the DWT-decomposed image, specifically in 3 × 3 block regions selected based on gradient information. The mathematical relationships between the gradient’s direction and magnitude are employed to extract the corresponding blocks and their codes adequately. These codes are then XORed with the watermark and embedded into the chosen blocks using the least significant bit (LSB) technique. Extensive experimentation on a medical image dataset evaluates the system’s performance against some attacks like filtering, noise, and scaling. The results demonstrate the efficacy of the proposed approach in hiding information while ensuring the security and integrity of watermarked medical images.

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Detection and localization for watermarking technique using LSB encryption for DICOM Image

Cited by 3 publications

References 14 publications

COVID-19: respiratory disease diagnosis with regularized deep convolutional neural network using human respiratory sounds

COVID-19: respiratory disease diagnosis with regularized deep convolutional neural network using human respiratory sounds

Secure NIfTI Image Authentication Scheme for Modern Healthcare System

A DWT-Based Approach with Gradient Analysis for Robust and Blind Medical Image Watermarking

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