Block-wise reversible data hiding in encrypted domain using SVD

The properties of singular value decomposition (SVD) are used to implement an SVD spatial domain pseudoinverse restoration filter. This type of filter is attractive for poor imaging conditions (low spatial resolution, high image noise) and is thus appealing for nuclear medicine images. The method might offer some advantages over more traditional frequency domain filter techniques since the restoration is performed on a local rather than global basis. High-contrast thyroid phantom images collected at different count densities and low-contrast liver phantom images were processed with the SVD filter. Restored images yielded improved spatial resolution, lesion contrast, and signal-to-noise ratio. The SVD pseudoinverse restoration filter implemented as an interactive process permits the operator to terminate filtering at a stage where the visually "best" image is obtained compared to the original data. Processed images suggest that the technique may have potential for improving lesion detection in nuclear medicine.

In this paper, two new properties of singular value decomposition (SVD) on images are proved. The first property demonstrates the quantitative relationship between singular values and power spectrum. The second one proves that under the condition of losing equal power spectrum, the square-error of the reconstructed image is much smaller when we reduce all singular values proportionally instead of neglect the smaller ones. Based on the two properties, a new data-hiding scheme is proposed. It performs well as for robustness, for it satisfies power-spectrum condition (PSC), and PSC-compliant watermarks are proven to be most robust. Besides, the proposed scheme has a good performance as for capacity and adaptability.

The security of information exchange is very important on the network. Authentication and information hiding have also become important issues. Information hiding techniques are acquiring an increasing importance due to the widespread diffusion of multimedia contents. The aim of this chapter is to focus on the Singular Value Decomposition (SVD) transform, with the aim of providing an exhaustive overview on those steganography, image cryptography and watermarking techniques leveraging on the important properties of such a transform. Despite the attention it has received in the last years, SVD in image processing and security is still in its infancy. Many SVD characteristics are still unutilized in image processing. In this chapter the author tries to highlight the basic properties of SVD and some of their applications in the field of security to encourage researchers to discover more about SVD properties which are not yet utilized.

In this article, we have improved the quantitative inversion performance of the cross-correlated contrast source inversion (CC-CSI) method by incorporating the subspace optimization strategy. The proposed method is called the cross-correlated subspace optimization method (CC-SOM). Meanwhile, multifrequency data are used to improve the inversion performance of high-contrast scatterers, where the L-curve method is introduced to select the regularization parameters of each frequency point without relying on experience. Finally, a fast algorithm is implemented by using the property of singular value decomposition (SVD) to simplify the large-scale matrix, and the fast Fourier transform (FFT) to accelerate the calculation. Synthetic and experimental inversion results demonstrate that both CC-SOM and CC-CSI show better robustness than SOM. In comparison to CC-CSI, CC-SOM is superior in terms of inversion accuracy when the regularization parameters have been appropriately selected. However, these advantages come at the cost of higher computational complexity.

Author profiling is a challenging task that consists of identifying a person’s relevant attributes based on the content he/she generates. In this article, we validate a classification method based on the reconstructive classification to identify two demographic attributes, age and gender, for users of social networks based on the text content they publish. For the problem, we consider balanced and unbalanced data, where a gender or age group has a larger presence than the others. The proposed method is based on using the reconstructive property of singular value decomposition (SVD), and its suitability to work with sparse data, to find a matrix with the main latent components to represent the information of specific gender or age classes. Afterward, we use such a matrix to project and reconstruct new users’ information to identify their demographic variables. We test our method with a set of datasets in several languages collected from Twitter and Pinterest users, and we use different evaluation metrics to compare their performance with the ones of several popular classifiers based on traditional machine learning, and on deep learning, and with some relevant works in the literature. The results show that the proposed classifier performs generally well in identifying the age and gender of users in social networks.

We propose a technique for video shot segmentation and classification based on singular value decomposition (SVD). For the input video sequence, we create a feature-frame matrix A, and perform the SVD on it. From this SVD, we are able to not only derive the refined feature space to better segment the video sequence along the time axis, but also define metrics to enable classifications of the detected video shots. Using these SVD properties, we achieve the two goals of accurate video shot segmentation, and visual content-based shot classification at the same time.

A new approach to transparent embedding of data into digital images is proposed. It provides a high rate of the embedded data and is robust to common and some intentional distortions. The developed technique employs properties of the singular value decomposition (SVD) of a digital image. According to these properties each singular value (SV) specifies the luminance of the SVD image layer, whereas the respective pair of singular vectors specifies image geometry. Therefore slight variations of SVs cannot affect the visual perception of the cover image. The proposed approach is based on embedding a bit of data through slight modifications of SVs of a small block of the segmented covers. The approach is robust because it supposes to embed extra data into low bands of covers in a distributed way. The size of small blocks is used as an attribute to achieve a tradeoff between the embedded data rate and robustness. An advantage of the approach is that it is blind. Simulation has proved its robustness to JPEG up to 40%. The approach can be used both for hidden communication and watermarking.KeywordsSingular Value DecompositionCover ImageSingular VectorJPEG CompressionHide DataThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

A secure image sharing scheme based on SVD and Fractional Fourier Transform

Image watermarking is a sophisticated method often used to assert ownership and ensure the integrity of digital images. This research aimed to propose and evaluate an advanced watermarking technique that utilizes a combination of singular value decomposition methodology and discrete cosine transformation to embed the Dian Nuswantoro University symbol as proof of ownership into digital images. Specific goals included optimizing the embedding process to ensure high fidelity of the embedded watermark and evaluating the fuzziness of the watermark to maintain the visual quality of the watermarked image. The methods used in this research were singular value decomposition and discrete cosine transformation, which are implemented because of their complementary strengths. Singular value decomposition offers robustness and stability, while discrete cosine transformation provides efficient frequency domain transformation, thereby increasing the overall effectiveness of the watermarking process. The results of this study showed the efficacy of the Lena image technique in gray scale having a mean square error of 0.0001, a high peak signal-to-noise ratio of 89.13 decibels (dB), a universal quality index of 0.9945, and a similarity index structural of 0.999. These findings confirmed that the proposed approach maintains image quality while providing watermarking resistance. In conclusion, this research contributed a new watermarking technique designed to verify institutional ownership in digital images, specifically benefiting Dian Nuswantoro University. It showed significant potential for wider application in digital rights management.

We often need to transmit and store the images in many applications. Smaller the image, less is the cost associated with transmission and storage. So we often need to apply data compression techniques to reduce the storage space consumed by the image. One approach is to apply Singular Value Decomposition (SVD) on the image matrix. In this method, digital image is given to SVD. SVD refactors the given digital image into three matrices. Singular values are used to refactor the image and at the end of this process, image is represented with smaller set of values, hence reducing the storage space required by the image. Goal here is to achieve the image compression while preserving the important features which describe the original image. SVD can be adapted to any arbitrary, square, reversible and non-reversible matrix of m × n size. Compression ratio and Mean Square Error is used as performance metrics.

This paper presents a new robust hybrid multiple watermarking technique using fusion of discrete wavelet transforms (DWT), discrete cosine transforms (DCT), and singular value decomposition (SVD) instead of applying DWT, DCT and SVD individually or combination of DWT-SVD / DCT-SVD. For identity authentication purposes, multiple watermarks are embedded into the same medical image / multimedia objects simultaneously, which provides extra level of security with acceptable performance in terms of robustness and imperceptibility. In the embedding process, the cover image is decomposed into first level discrete wavelet transforms where the A (approximation/lower frequency sub-band) is transformed by DCT and SVD. The watermark image is also transformed by DWT, DCT and SVD. The S vector of watermark information is embedded in the S component of the cover image. The watermarked image is generated by inverse SVD on modified S vector and original U, V vectors followed by inverse DCT and inverse DWT. The watermark is extracted using an extraction algorithm. Furthermore, the text watermark is embedding at the second level of the D (diagonal sub-band) of the cover image. The security of the text watermark considered as EPR (Electronic Patient Record) data is enhanced by using encryption method before embedding into the cover. The results are obtained by varying the gain factor, size of the text watermark, and cover medical images. The method has been extensively tested and analyzed against known attacks and is found to be giving superior performance for robustness, capacity and reduced storage and bandwidth requirements compared to reported techniques suggested by other authors.

We present a new approach for transparent and high rate embedding of watermarks into digital images using fast Hadamard transform (FHT) and singular value decomposition (SVD). The proposed algorithm consists of three main steps: dividing the cover image into small blocks, applying the FHT to each block, and distributing the singular values of the visual watermark image over the transformed cover blocks. The main attractive features of this approach are simplicity, flexibility in data embedding capacity, and real-time implementation. The experimental results show the much improved performance of the proposed method in comparison with existing techniques, and also its robustness against the most common attacks

Blind digital image forensics, aims at identifying the authenticity and origin of the digital images without any embedding information beforehand, has become one of the research hot spots recently. Resampling, as a kind of tampering manipulation, will change the statistical features of the images. In this paper, the specific statistical changes brought into the linear dependencies among image pixels are analyzed by singular value decomposition. A blind and efficient method capable of finding traces of resampling and interpolation based on singular value decomposition is proposed. The performance of resampling detection shown in experimental results has indicated the validity of the algorithm.

This contribution applies tools from the information theory and soft computing (SC) paradigms to the embedding and extraction of watermarks in aerial remote sensing (RS) images to protect copyright. By the time 5G came along, Internet usage had already grown exponentially. Regarding copyright protection, the most important responsibility of the digital image watermarking (DIW) approach is to provide authentication and security for digital content. In this paper, our main goal is to provide authentication and security to aerial RS images transmitted over the Internet by the proposal of a hybrid approach using both the redundant discrete wavelet transform (RDWT) and the singular value decomposition (SVD) schemes for DIW. Specifically, SC is adopted in this work for the numerical optimization of critical parameters. Moreover, 1-level RDWT and SVD are applied on digital cover image and singular matrices of LH and HL sub-bands are selected for watermark embedding. Further selected singular matrices SLH and SHL are split into 3×3 non-overlapping blocks, and diagonal positions are used for watermark embedding. Three-level symmetric encryption with low computational cost is used to ensure higher watermark security. A hybrid grasshopper–BAT (G-BAT) SC-based optimization algorithm is also proposed in order to achieve high quality DIW outcomes, and a broad comparison against other methods in the state-of-the-art is provided. The experimental results have demonstrated that our proposal provides high levels of imperceptibility, robustness, embedding capacity and security when dealing with DIW of aerial RS images, even higher than the state-of-the-art methods.

For the last couple years, governments and health authorities worldwide have been focused on addressing the Covid-19 pandemic; for example, governments have implemented countermeasures, such as quarantining, pushing vaccine shots to minimize local spread, investigating and analyzing the virus’ characteristics, and conducting epidemiological investigations through patient management and tracers. Therefore, researchers worldwide require funding to achieve these goals. Furthermore, there is a need for documentation to investigate and trace disease characteristics. However, it is time consuming and resource intensive to work with documents comprising many types of unstructured data. Therefore, in this study, natural language processing technology is used to automatically classify these documents. Currently used statistical methods include data cleansing, query modification, sentiment analysis, and clustering. However, owing to limitations with respect to the data, it is necessary to understand how to perform data analysis suitable for medical documents. To solve this problem, this study proposes a robust in-depth mixed with subject and emotion model comprising three modules. The first is a subject and non-linear emotional module, which extracts topics from the data and supplements them with emotional figures. The second is a subject with singular value decomposition in the emotion model, which is a dimensional decomposition module that uses subject analysis and an emotion model. The third involves embedding with singular value decomposition using an emotion module, which is a dimensional decomposition method that uses emotion learning. The accuracy and other model measurements, such as the F1, area under the curve, and recall are evaluated based on an article on Middle East respiratory syndrome. A high F1 score of approximately 91% is achieved. The proposed joint analysis method is expected to provide a better synergistic effect in the dataset.