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Healthcare institutions adapt cloud based archiving of medical images and patient records to share them efficiently. Controlled access to these records and authentication of images must be enforced to mitigate fraudulent activities and medical errors. This paper presents a zero-watermarking scheme implemented in the composite Contourlet Transform (...
Citations
... Due to the QR code's characteristics, QR code has been applied to digital watermarking to protect copyright in recent years [20][21][22]. But QR codes can't support the storage of images and files. ...
... Also, the S matrix is invariant to transposing, flipping, scaling, rotation, and translation. Smaller modifications to the images do not significantly change their singular values [21]. ...
Medical images are important records to improve people’s health care, and their security has become an important issue when they are transmitted through various media especially print and scan equipments. This paper proposes a digital watermarking method for medical images based on live QR code, DWT-SVD, Hough transform and bilinear interpolation. A live QR code including private information was embedded into the SVD blocks of LL3sub-band wavelet of medical image. Adjusting the embedding strength, a trade-off between invisibility and robustness was achieved. We printed and scanned the watermarked medical image with common printers and scanners and get the watermarked images after print-scanning. In extraction method, the Hough transform was used to detect the edge of the watermarked medical image and calculate the rotation angle and scaling ratio, and bilinear interpolation was used to correct geometric distortion of watermarked image. Then we extracted live QR code from corrected image successfully. Experimental results indicated that the proposed method provides sufficient security for medical images against print and scan attacks.
... Where patient information or doctor signature and source information are represented in binary format and embedded into host image for security purpose and to achieve better robustness. To authenticate the patient record, Seenivasagam et al. [41] developed a framework using countourlet transform in SVD domain. This scheme uses quick response (QR) code as a watermark to embed electronic patient record (EPR). ...
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.
... Zero-watermarking takes some intrinsic information from the host image instead of applying a sequence of watermarks. The watermark sequence of the owner connects these inherent properties to create a master share that is securely stored [35,39,60]. The owner can demonstrate ownership of the protected images using the zero watermark, which can be conveyed via any unsecured public communication channel. ...
Robust zero-watermarking is a protection of copyright approach that is both effective and distortion-free, and it has grown into a core of research on the subject of digital watermarking. This paper proposes a revolutionary zero-watermarking approach for color images using convolutional neural networks (CNN) and a 2D logistic-adjusted Chebyshev map (2D-LACM). In this algorithm, we first extracted deep feature maps from an original color image using the pre-trained VGG19. These feature maps were then fused into a featured image, and the owner's watermark sequence was incorporated using an XOR operation. Finally, 2D-LACM encrypts the copyright watermark and scrambles the binary feature matrix to ensure security. The experimental results show that the proposed algorithm performs well in terms of imperceptibility and robustness. The BER values of the extracted watermarks were below 0.0044 and the NCC values were above 0.9929, while the average PSNR values of the attacked images were 33.1537 dB. Also, it is superior to other algorithms in terms of robustness to conventional image processing and geometric attacks.
... The methodology involves the utilization of some selected algorithms to test variety of medical image where the results show zero watermarking effectiveness on the detection of modification but ineptitude on authorship of different patient image. The experimentation of this analysis conducted using four zero watermarking algorithms [24][25][26][27]. ...
... Medical image watermarking for authentication and tamper detection, a fragile watermarking scheme where patient information in the form of EPR is embedded into the host image for protection against wrong diagnosis and treatment. The evaluation of existing research with fifteen (15) [2,12,27,32,33,[35][36][37][38][39][40][41][42][43][44] work shows that authentication, tamper detection, localization and recovery application dominates medical imaging environments or communities such as hospitals. Ouazzane et al. described the Authentication and integrity verification as a reliability approach, a fragile watermarking with tamper detection, localization, and tamper recovery. ...
Medical images are fundamentally utilized for rendering diagnostic and treatment to patients. Medical images are a patient body or part captured using medical imaging devices such as CT, X-Ray, PET, MRI, and US. Technological advancement introduces E-Healthcare systems, Telemedicine, and Electronic Health Information Systems (EHIS) enabling medical images to flow over the public network for remote healthcare services. The manipulation or replacement of medical images is fatal to the well-being of a patient, thereby requiring protection using watermarking. Watermarking is a data security approach toward protecting medical images against abuse by unauthorized personnel via providing confidentiality, authentication, and integrity verification. The dynamism and importance of medical image watermarking require constant literature update on trends, issues, and challenges which leading to the forgoing research survey. The survey proposes to highlight trending application areas in medical image watermarking research and evaluation of the recent approach adopted by researchers. Furthermore, the survey evaluates existing work in compliance with the standard benchmark requirement in design and performance and presents a discussion on the way forward to other possible research opportunities in the medical image watermarking domain.
... The results show good peak signal to noise ratio (PSNR) and low normalized correlation (NC) compared to the same hybrid combination with firefly optimization. The Quick Response (QR) code was used as a watermark in [15], which proposes a blind watermarking scrambling the QR through Arnold transform before implanting the hidden information in the host signal. Different metrics were used to check the imperceptibility and to verify the robustness such as similarity index measure (SSIM), NC and Bit Error Rate. ...
Multimedia security has received much attention recently because of the rapid transmission of elements such as text, images, audio, video, software, animation and games. Security is becoming especially critical for content owners concerned about the illegal usage of their original products. Encryption and watermarking are two methodologies for digital applications. Spatial domain and frequency domain watermarking algorithms give very promising results in embedding binary images into the cover images. This paper proposed a new method of semi-blind watermarking technique. The digital images are divided into 4 x 4 blocks and converted into discrete Wavelet transformations (DWTs). The binary image is embedded into each block using the flexible scaling factor method. Experimental results show that the proposed method has higher peak signal to noise ratio (PSNR) and similarity ratio (SR) values compared to the standard Wavelet transformation and block-based Wavelet algorithms. The results prove that the proposed hybrid algorithm is more effective, robust, secure and resistant than DWT and block-based DWT algorithms.
... And in order to reduce the impact of noise on the watermark image, Back Propagation Neural Network (BPNN) is applied to the extracted image watermark; Han et al. (2015) used three-dimensional discrete wavelet transform and three-dimensional discrete cosine transform to extract medical image features. It combined with perceptual hash technology to obtain medical image feature sequences to embed and extract watermarks, and, use Legendre chaotic neural network to generate chaotic sequences encrypt the watermark to improve the security of the watermark; Seenivasagam and Velumani (2013) proposed a medical image watermarking algorithm based on composite Contourlet Transform (CT) and Singular Value Decomposition (SVD), using Hu invariants to represent image features and implementing watermark encryption through Arnold transform. For the watermarking algorithm proposed in Thakkar and Srivastava (2017), Thanki et al. (2017), Zear et al. (2016), they have good robustness against common attacks and perform well in terms of watermark capacity, but are less robust to geometric attacks; moreover, the embedding of watermarks will affect the image itself. ...
... For medical images, any modification that affects the doctor's diagnosis is not allowed. The watermarking algorithm proposed in Han et al. (2015), Seenivasagam and Velumani (2013) are all zero watermarking algorithms, the embedding and extraction of watermarks do not need to modify the medical image itself. However, although Han et al. (2015) has good robustness against common attacks, the watermark capacity is relatively small, and the method of Seenivasagam and Velumani (2013) has higher algorithm complexity. ...
... The watermarking algorithm proposed in Han et al. (2015), Seenivasagam and Velumani (2013) are all zero watermarking algorithms, the embedding and extraction of watermarks do not need to modify the medical image itself. However, although Han et al. (2015) has good robustness against common attacks, the watermark capacity is relatively small, and the method of Seenivasagam and Velumani (2013) has higher algorithm complexity. Therefore, finding an algorithm that does not need to modify the original data of the medical image and can show good robustness to geometric attacks has always been the main topic of research by researchers. ...
With the wide application of digital watermarking technology in the field of medical imaging, the security of medical image information has been improved. Aiming at the problem of poor robustness of medical image watermarking algorithms against geometric attacks, the security of medical images cannot be guaranteed. This paper proposed a zero watermarking algorithm for medical images based on KAZE-DCT. First, KAZE-DCT is used to extract feature vectors of medical images, and perceptual hashing is used to obtain feature sequences of medical images. Then, chaotic mapping is used to encrypt the multi-watermark images, and the zero watermarking technology is applied to embed and extract the watermarks. Finally, the correlation coefficient is used to measure the correlation between the algorithm embedding and extracting the watermarks. Experimental results show that the algorithm can effectively extract watermarks. Moreover, it has good robustness against both common attacks and geometric attacks.
... Dong et al. [21] design another low-frequency DCT coefficient-based zero watermarking scheme for better robustness. Seenivasagam et al. [22], [23] exploit Contourlet transform (CT) and deploy the first 3 and 6 Hu invariant moment sign bits in the CT-SVD domain to extract features. Fan et al. [24] design a zero watermarking algorithm based on Cellular Automate Transformation (CAT) for copyright protection to achieve sufficient security and robustness. ...
... P fn = N fn N ts (22) Here, N fp is the number of different medical image pairs whose inter-BER is smaller than a predefined threshold T h , N td is the true number of different medical image pairs, N fn is the number of original medical image pairs and its attacked medical image whose intra-BER is larger than T h , and N ts represents the true number of attacked medical images. ...
... In this section, we made the comparison of our proposed watermarking scheme and other existing zero-watermarking schemes, which are Zou et al. [25], Seenivasagam et al. [22], Du et al. [26] and Liu et al. [27]. First of all, we use inter-BERs to evaluate the distinguishability of our proposed scheme. ...
The verification of copyright and authenticity for medical images is critical in telemedical applications. Watermarking is a key technique for protecting medical images and can be mainly divided into three categories: region of interest (ROI) lossless watermarking, reversible watermarking and zero-watermarking. However, ROI lossless watermarking causes biases on diagnosis. Reversible watermarking can hardly provide a continuous verification function and may face verification disputes after image recovering. Zero-watermarking requires third-party storage which may cause additional security problems. To address these issues, a hybrid reversible-zero watermarking (HRZW) is proposed in this paper to effectively combine the complementary advantages of reversible watermarking and zero-watermarking. In our scheme, a novel hybrid structure is designed including a zero-watermarking component and a reversible watermarking component. In the first component, ownership share is generated by mapping nearest neighbor grayscale residual (NNGR) based features and watermark information. In the second component, the generated ownership share is embedded reversibly based on Slantlet Transform, Singular Value Decomposition and Quantization Index Modulation (SLT-SVD-QIM). Experimental results demonstrate that our proposed scheme not only yields remarkable watermarking imperceptibility, distinguishability and robustness, but also provides continuous verification function without any dispute or third-party storage, which outperforms existing watermarking schemes for medical images.
... In the embedding stage, the cover image pixels are modified during the reversible watermark, but at the receiver, the cover image can be restored by extracting the watermark [11]. The watermark embedding method does not allow any changes to the pixels of the cover image in the zero watermark, several main features need to be taken out of the cover image and used as a guide during data authentication [12]. However, the limitation of the zero-watermark is that in order to perform potential data identity verification, the specific attributes extracted from each cover image must be kept secret. ...
In telemedicine, the realization of reversible watermarking through information security is an emerging research field. However, adding watermarks hinders the distribution of pixels in the cover image because it creates distortions (which lead to an increase in the detection probability). In this article, we introduce a reversible watermarking method that can transmit medical images with minimal distortion and high security. The proposed method selects two adjacent gray pixels whose least significant bit (LSB) is different from the relevant message bit and then calculates the distortion degree. We use the LSB pairing method to embed the secret matrix of patient record into the cover image and exchange pixel values. Experimental results show that the designed method is robust to different attacks and has a high PSNR (peak signal-to-noise ratio) value. The MRI image quality and imperceptibility are verified by embedding a secret matrix of up to 262,688 bits to achieve an average PSNR of 51.657 dB. In addition, the proposed algorithm is tested against the latest technology on standard images, and it is found that the average PSNR of our proposed reversible watermarking technology is higher (i.e., 51.71 dB). Numerical results show that the algorithm can be extended to normal images and medical images.
... -Le BER dans l'approche proposée est égal en moyenne à 3%, alors qu'il est égal en moyenne à 18,5%, 13%, 5%, 19%, 3%, 3,7%, 21,1% dans [ [50], [142], [139], [138], [161], [46], [41]] respectivement. ...
Le développement rapide des technologies multimédia et de communication permet de faciliter le partage et l’accès à distance aux données des patients, notamment en télémédecine. La demande de sécurité des images médicales augmente. Pour les applications de télémédecine, il ne s’agit pas seulement de garantir la confidentialité et la fiabilité (intégrité et authenticité) des images, mais aussi de fournir des preuves dans le cas où il y’a une modification illicite des données. Le tatouage numérique d’image est l’une des technologies cherchant à protéger les images médicales lors de la transmission sur un réseau public non sécurisé. Il permet d’insérer ou masquer une marque dans une image numérique de manière invisible sans dégrader visuellement la qualité de l’image dans le but d’assurer l’intégrité, l’authentification et la confidentialité des images et des données des patients dans les applications d’imagerie médicale. Dans cette thèse, nous avons travaillé sur la sécurité des images médicales en nous basant sur des solutions de tatouage numérique ainsi que des solutions combinant le tatouage numérique et la cryptographie. Une première contribution de ce travail concerne une approche de zéro-tatouage pour l’authentification des images DICOM. Une deuxième contribution est une approche qui combine une méthode de zéro-tatouage dans la partie ROI de l’image et une méthode d’insertion de marque dans la partie RONI de l’image pour l’authentification des images médicales. Une troisième contribution est une approche d’authentification forte et résistante aux clones pour le système d’images médicales combinant une technique de tatouage réversible et une technique de cryptographie physique appelée SUC (Secret Unckown Ciphers). Les approches proposées sont robustes et résistantes à différents types d’attaques. Elles couvrent les caractéristiques de sécurité suivantes : intégrité, confidentialité, authentification. Elles permettent d’assurer la non modification de la partie de l’image utilisée pour le diagnostic, et garantissent ainsi aux médecins un diagnostic non entaché d’erreur. De plus, la réversibilité du tatouage proposé dans la troisième approche garantit la récupération de l’image médicale originale lors de la phase d’extraction.
... In [79], the authors provided unconditional authentication in medical images by presenting a zero watermarking scheme using composite Contourlet Transform (CT) in SVD domain. Watermark used in this method is a Quick Response (QR) code in which the patient's identification details and Electronic Patient Record (EPR) are encoded. ...
With the widespread growth of medical images and improved communication and computer technologies in recent years, authenticity of the images has been a serious issue for E-health applications. In order to this, various notable watermarking techniques are developed by potential researchers. However, those techniques are unable to solve many issues that are necessary to be measured in future investigations. This paper surveys various watermarking techniques in medical domain. Along with the survey, general concepts of watermarking, major characteristics, recent applications, concepts of embedding and recovery process of watermark, and the summary of various techniques (in tabular form) are highlighted in brief. Further, major issues associated with medical image watermarking are also discussed to find out research directions for fledgling researchers and developers.
