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... Then at last, digest is compared to check whether it is tampered or not. To ensure that the watermarking process is reliable and undetectable, iterative approach is proposed by Madhu et al. [99]. Stationary wavelet transformation (SWT) and SVD is applied on both host image and watermark image. ...
In order to aim for diagnosis of disease and decision making, the medical imaging plays an important role in health science. Clinical pictures are portrayals of profoundly vulnerable computerized images, those can be tampered without leaving any visual clues. Hence it is challenging to keep up its credibility. However, as there are numerous ways to manipulate an image, correspondingly various strategies have also been proposed to safeguard the genuineness of medical images. This survey paper presents different techniques used for medical image authentication viz. watermarking, signature and hybrid techniques. The state-of-the-art techniques have attained promising results for authentication and tampering detection, but an efficient tampering localization and recovery have remained still as a challenge. This review article can be considered as a benchmark survey paper as it gives a complete comprehensive overview commencing from the evolution of medical image formats, types of medical imaging modalities and also provides an elaborative comparison over 40 research works in terms of prominent factors like type of medical image used, embedded region, embedded data, about the coverage of tampering localization and recovery along with the discussion on limitations and future works of each one.
The goal of security is to protect digital assets, devices and services from being disrupted, exploited or stolen by unauthorised users. It is also about having reliable media information available at the right time. However, the media distortion will pose many potential risks in eHealth systems. In this paper, a new data hiding method called EmbedR-Net based on Convolutional Neural Network (CNN) and Deep Convolutional Generative Adversarial Network (DCGAN) is proposed, which can prevent the copyright violation of the medical images. First, a CNN based embedder network is designed for imperceptibly hiding medical images as marks in the carrier image. Second, we compute the diagonal value of the marked image for hidden mark recovery. Last, the DCGAN network is designed to robustly recover the hidden mark using the diagonal value of the marked image. Compared to existing methods, experiments on five different datasets have shown that the proposed EmbedR-Net obtains superior performance.
Medical image analysis using deep learning algorithm (CNN) area is of foremost significance and maybe it is anything but a high need area. A great deal of concern is as yet needed in this area. To be sure, in larger part of cases, the medical information is deciphered by human master, whilst examination of medical information is very demanding and muddled errand. Regularly, discrete examination is performed by various human specialists which brings about mistaken identification of illness. The astounding exhibition of deep learning (DL) in various domains pulled in the specialists to apply this method inside the domain of medical area as DL gives incredible exactness and precision in conclusive yield. Hence, it has been imagined as a centre strategy for medical image analysis using deep learning algorithm (CNN) and in different floods of the medical services area. Further, division measure is the basic, viable and centre advance of the medical image investigation. Scientists are reliably endeavouring to increase the precision of medical image examination. In ongoing past, machine knowledge-based strategies have generally been utilised for this pursuit. The new pattern in the domain of medical image examination is the ramifications of profound learning-based methodologies. Maybe, the use of profound learning improves the prescient correctness of the individual. Additionally, it likewise mitigates the intercession of human specialists in the analysis marvel. This paper aims to survey on medical image analysis using deep learning algorithm convolutional neural network (CNN). The use of deep learning algorithms for medical image analysis is very significant since it is producing enough and reliable results comparing to human tasks; it also reduces human work and time; basing on all these aspects, a survey is about to done. In this paper, besides medical image analysis, convolutional neural network (CNN) architectural implementation and its features are also discussed.KeywordsImage analysisConvolutional neural networksClassificationSegmentation
