Securing Medical Data by Combining Encryption and Robust Blind Medical Image Watermarking Based on Zaslavsky Chaotic Map and DCT Coefficients

2022 ◽  
Vol 3 (2) ◽  
Author(s):  
Nawal Balaska ◽  
Aissa Belmeguenai ◽  
Ahcène Goutas ◽  
Zahir Ahmida ◽  
Selma Boumerdassi
2021 ◽  
Vol 38 (6) ◽  
pp. 1637-1646
Author(s):  
KVSV Trinadh Reddy ◽  
S. Narayana Reddy

In distributed m-health communication, it is a major challenge to develop an efficient blind watermarking method to protect the confidential medical data of patients. This paper proposes an efficient blind watermarking for medical images, which boasts a very high embedding capacity, a good robustness, and a strong imperceptibility. Three techniques, namely, discrete cosine transform (DCT), Weber’s descriptors (WDs), and Arnold chaotic map, were integrated to our method. Specifically, the Arnold chaotic map was used to scramble the watermark image. Then, the medical image was partitioned into non-over lapping blocks, and each block was subjected to DCT. After that, the scrambled watermark image data were embedded in the middle-band DCT coefficients of each block, such that two bits were embedded in each block. Simulation results show that the proposed watermarking method provides better imperceptibility, robustness, and computational complexity results with higher embedding capacity than the contrastive method.


2018 ◽  
Vol 7 (3.12) ◽  
pp. 711
Author(s):  
S Priya ◽  
B Santhi ◽  
J Raja Mohan

In telemedicine, medical data are shared across the world among different specialists for various purposes through an unsecured medium. So there is a need to protect the medical data during transmission. With the help of image watermarking techniques, medical images are protected along with the electronic patient information (EPI). This paper proposes a medical image watermarking, by applying wavelet transform, using an interpolation technique. EPI data is embedded within the transformed medical image to generate a watermarked image. At the extraction side, EPI data are extracted and medical image is reconstructed without any loss. The performance of the proposed method is analyzed using a peak signal to noise ratio (PSNR), mean absolute error (MAE) and structural similarity index (SSIM).   The experimental result shows that the proposed method gives better results.


2021 ◽  
pp. 1-16
Author(s):  
Narima Zermi ◽  
Amine Khaldi ◽  
Mohamed Redouane Kafi ◽  
Fares Kahlessenane ◽  
Salah Euschi

2017 ◽  
pp. 13-41 ◽  
Author(s):  
Amit Kumar Singh ◽  
Basant Kumar ◽  
Ghanshyam Singh ◽  
Anand Mohan

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