Tamper Detection and Self-Recovery of Medical Imagery for Smart Health

Hussan, Muzamil; Parah, Shabir A.; Gull, Solihah; Qureshi, G. J.

doi:10.1007/s13369-020-05135-9

Cited by 13 publications

(5 citation statements)

References 33 publications

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“…Figure 18 a and 18 b show the framework for data embedding and extraction in authentication-based watermarking schemes. The state-of-the-art techniques along with evaluation parameters, pros, and cons of authentication-based watermarking techniques for medical image watermarking have been presented in Table 8 [ 30 , 41 , 44 , 51 , 61 , 63 , 68 , 79 , 81 , 85 ]. Further in Table 9 we present the comparative analysis of several existing schemes in terms of PSNR, SSIM, and computational complexity.…”

Section: State Of the Art Watermarking Techniques For Medical Imagementioning

confidence: 99%

“… • The method is not robust against rotation attacks. Robust Hussan et al [ 30 ] Self embedding LSB substitution PSNR, SSIM, computational time, TR • Detects, localizes, and recovers tamper • The quality of recovered images is poor. Fragile Lin et al [ 41 ] AMBTC, VQ Compression PSNR,TR, FPR,FNR • Detects, localizes, and recovers tamper.…”

Section: State Of the Art Watermarking Techniques For Medical Imagementioning

confidence: 99%

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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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Section: State Of the Art Watermarking Techniques For Medical Imagementioning

confidence: 99%

Section: State Of the Art Watermarking Techniques For Medical Imagementioning

confidence: 99%

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

Gull

Parah

2023

Multimed Tools Appl

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“…Image watermarking methods have been proposed by various researchers [6,12,14,15,[19][20][21] to identify tampered areas in images. These methods not only determine if an image has been tampered with but also identify where the tampering occurred and can pinpoint the specific areas.…”

Section: Introductionmentioning

confidence: 99%

Reversible Medical Image Watermarking for ROI-Based Tamper Detection and Recovery

Arham¹,

Nugroho

Adji

2023

Preprint

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Medical image watermarking has become a widely studied topic in recent years. The method used to embed data into medical images must be reversible becouse tampered in medical images caused by irreversible embedding can lead to inaccurate diagnosis. Medical image watermark- ing should be able to identify the tampered area and recover it. This research work introduced a method for reversible watermarking of med- ical images. This method can embed data, detect any tampering that occurs, locate the area that has been tampered with, and then recov- ery that area of tampered in the medical image. The medical image is segmented into three sections. The first is the region of interest (ROI) for reversible embedding of patient records. The second is the region of non-interest (RONI) for reversible embedding of localization data for tampered and recovery areas. The third is the border area used for embedding coordinates of the selected ROI using Least Significant Bits (LSB). The proposed method has a capacity of up to 6 bpp of data and 12 % of the ROI area with very good visual quality of up to 50.86 dB. The watermarked image, when extracted, is identical from the original image except for the border area, provided that there is no tampering. If the ROI is tampered, the area of tampered can be identified and restored with results reaching up to +27.61 dB, with only a small difference in image quality -2.86 dB compared to the original image after extraction.

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“…There are two types of image authentication techniques: active [5] and passive [6]. The active techniques use the watermarks embedded inside the image to check the integrity [7][8][9][10][11], while the passive techniques do not use any prior knowledge about the image to detect forgery [12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

A Visual Cryptography-Based Watermarking Approach for the Detection and Localization of Image Forgery

2022

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The usage of images in different fields has increased dramatically, especially in medical image analysis and social media. Many risks can threaten the integrity and confidentiality of digital images transmitted through the internet. As such, the preservation of the contents of these images is of the utmost importance for sensitive healthcare systems. In this paper, the researchers propose a block-based approach to protect the integrity of digital images by detecting and localizing forgeries. It employs a visual cryptography-based watermarking approach to provide the capabilities of forgery detection and localization. In this watermarking scheme, features and key and secret shares are generated. The feature share is constructed by extracting features from equal-sized blocks of the image by using a Walsh transform, a local binary pattern and a discrete wavelet transform. Then, the key share is generated randomly from each image block, and the secret share is constructed by applying the XOR operation between the watermark, feature share and key share. The CASIA V 1.0 and SIPI datasets were used to check the performance and robustness of the proposed method. The experimental results from these datasets revealed that the percentages of the precision, recall and F1 score classification indicators were approximately 97% for these indicators, while the percentages of the TAF and NC image quality indicators were approximately 97% and 96% after applying several known image processing and geometric attacks. Furthermore, the comparative experimental results with the state-of-art approaches proved the robustness and noticeable improvement in the proposed approach for the detection and localization of image forgeries in terms of classification and quality measures.

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Tamper Detection and Self-Recovery of Medical Imagery for Smart Health

Cited by 13 publications

References 33 publications

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

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

Reversible Medical Image Watermarking for ROI-Based Tamper Detection and Recovery

A Visual Cryptography-Based Watermarking Approach for the Detection and Localization of Image Forgery

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