A robust video watermarking scheme using sparse principal component analysis and wavelet transform

In this paper, a new robust and secure video watermarking scheme based on discrete Wavelet Transform (DWT), singular value decomposition (SVD), and Laplacian pyramid is proposed for copyright protection applications. The main contributions of this work are: 1) Increasing watermark robustness towards common signal processing attacks by separating the watermark image into a high-frequency image and a low-frequency image using laplacian pyramid. Then, the singular values of each of the low-frequency watermark image and the high-frequency watermark image are hidden in two different DWT subbands of the original video frame. Separating the watermark and hiding its two parts in two different subbands of the DWT of the video frame makes it robust towards noise, filtering, and compression attacks. 2) Improving the quality of the extracted watermarks via correcting the received watermarked videos using SURF points when those videos are geometrically attacked, mainly by rotation, scaling, and shear attacks. 3) Increasing security and avoiding false positive problems (FPP) associated with SVD by using perceptual image hashing in the proposed scheme. Through experimental results and comparisons to other techniques, the proposed scheme has shown very high imperceptibility for the tested watermarked videos in terms of peak signal to noise ratio (PSNR). Also, the proposed scheme has demonstrated high watermark robustness towards various types of attacks, and an improvement in the quality of the extracted watermarks from 4 to 20 dB compared to other techniques.

Transcoding resilient video watermarking scheme based on spatio-temporal HVS and DCT

Previous versions of sparse principal component analysis (PCA) have presumed that the eigen-basis (a p × k matrix) is approximately sparse. We propose a method that presumes the p × k matrix becomes approximately sparse after a k × k rotation. The simplest version of the algorithm initializes with the leading k principal components. Then, the principal components are rotated with an k × k orthogonal rotation to make them approximately sparse. Finally, soft-thresholding is applied to the rotated principal components. This approach differs from prior approaches because it uses an orthogonal rotation to approximate a sparse basis. One consequence is that a sparse component need not to be a leading eigenvector, but rather a mixture of them. In this way, we propose a new (rotated) basis for sparse PCA. In addition, our approach avoids “deflation” and multiple tuning parameters required for that. Our sparse PCA framework is versatile; for example, it extends naturally to a two-way analysis of a data matrix for simultaneous dimensionality reduction of rows and columns. We provide evidence showing that for the same level of sparsity, the proposed sparse PCA method is more stable and can explain more variance compared to alternative methods. Through three applications—sparse coding of images, analysis of transcriptome sequencing data, and large-scale clustering of social networks, we demonstrate the modern usefulness of sparse PCA in exploring multivariate data. An R package, epca, and the supplementary materials for this article are available online.

Now a day's large amount of digital information such as video, audio or image is spread over the internet. So it should be a great challenge for all researchers to provide security to such a type of digital data. Also the piracy and copyright is emerging issue when digital information in the form of video, audio & image is transmitted over the transmission media. So to prevent the problem of piracy and security of digital data such as video, video watermarking techniques has been introduced. There are various watermarking schemes to give the security to videos but they were unable to provide the quality in terms of robustness and security. In this paper optimized and hybrid based robust digital video watermarking scheme has been proposed. The optimization is performed in the proposed system with the help of genetic algorithm. The quality of the proposed watermarking scheme is measured in terms of PSNR and NC values. The PSNR calculated using our approach is off 50.48 db which is far better than existing approaches.

Most of the existing procedures for sparse principal component analysis (PCA) use a penalty function to obtain a sparse matrix of weights by which a data matrix is post-multiplied to produce PC scores. In this paper, we propose a new sparse PCA procedure which differs from the existing ones in two ways. First, the new procedure does not sparsify the weight matrix. Instead, the so-called loadings matrix is sparsified by which the score matrix is post-multiplied to approximate the data matrix. Second, the cardinality of the loading matrix i.e., the total number of nonzero loadings, is pre-specified to be an integer without using penalty functions. The procedure is called unpenalized sparse loading PCA (USLPCA). A desirable property of USLPCA is that the indices for the percentages of explained variances can be defined in the same form as in the standard PCA. We develop an alternate least squares algorithm for USLPCA which uses the fact that the PCA loss function can be decomposed as a sum of a term irrelevant to the loadings, and another one being easily minimized under cardinality constraints. A procedure is also presented for selecting the best cardinality using information criteria. The procedures are assessed in a simulation study and illustrated with real data examples.

Watermarking is one of the widely used applications to provide security for the content shared over internet. Internet is a place where the data is not of assured security to the fullest. DTCWT is widely used in all image processing applications over a decade. A novel imperceptible video watermarking scheme is proposed. This method is implemented with the use of Dual Tree Complex Wavelet Transform (DTCWT) and Singular value decomposition (SVD) which helps in proof of ownership. In this method Singular Value Decomposition is applied to the Dual Tree Complex Wavelet Transform (DTCWT) coefficients of both watermark and original image and the singular Eigen values are interchanged. Because of advantages, Shift invariance and Directionality we prefer DTCWT. As SVD decomposes the matrix into 3 matrices U, S, V, we need U, V at the time of extraction. So we use U, V as watermark in audio. Watermark extracted from the audio gives the U, V matrices which makes this method blind watermarking. In order to prove the robustness of the method the results are compared with similar algorithms proposed in this paper but with DWT.

The importance of Satellite Aperture Radar (SAR) imagery systems is increasing day-by-day in various field such as earth observation, hi-technology war mechanisms, etc. The images captured by SAR imagery systems are mainly used to detect and classify objects captured in the images. Due to the complexity of the image capturing process, SAR images can be highly noisy – often consisting of multiplicative noise, also known as speckle. To detect or classify objects in SAR images, speckle noise must be removed from images. During despeckling process, the preservation of important information in the SAR images while removing noise such as edge or patterns is a crucial task. Despeckling methods are liable to compromise on edge preservation ability while aspiring for good quality denoised images. Many researchers have proposed wavelet transform based despeckling approaches such as Discrete Wavelet Transform (DWT), Undecimated Wavelet Transform (UDWT), Dual Tree Complex Wavelet Transform (DTCWT). In these approaches, finding the best values of the coefficients plays an important role in yielding excellent denoised images with preserved edges. In this paper, we have proposed a novel optimization framework that optimizes thresholding coefficients of DTCWT despeckling method for SAR images. The proposed optimization framework is based on Fruit Fly Optimization (FOA) algorithm. The approach is a multi-objective optimization algorithm that is used to find maximum values for Peak Signal-to-Noise Ratio (PSNR), Mean Structural Similarity Index (MSSIM) and Equivalent number of look (ENL). The maximum value of MSSIM indicates high edge preservation capacity, whereas maximum PSNR value indicates good quality denoising of images. The maximum ENL value represents a good speckle-noise smoothing capability. We have applied our framework over some classical images as well as over SAR images of MSTAR dataset. In our experiments, we found that our proposed framework results in the excellent PSNR 36.87 dB, 35.4 dB and 37.8 dB, MSSIM values 0.92, 0.93 and 0.92, respectively, in the case of lena image, MSTAR dataset image and TerraSAR-X dataset image.

Abnormal glucose variability (GV) is a risk factor for diabetes complications, and tens of indices for its quantification from continuous glucose monitoring (CGM) time series have been proposed. However, the information carried by these indices is redundant, and a parsimonious description of GV can be obtained through sparse principal component analysis (SPCA). We have recently shown that a set of 10 metrics selected by SPCA is able to describe more than 60% of the variance of 25 GV indicators in type 1 diabetes (T1D). Here, we want to extend the application of SPCA to type 2 diabetes (T2D). A data set of CGM time series collected in 13 T2D subjects was considered. The 25 GV indices considered for T1D were evaluated. SPCA was used to select a subset of indices able to describe the majority of the original variance. A subset of 10 indicators was selected and allowed to describe 83% of the variance of the original pool of 25 indices. Four metrics sufficient to describe 67% of the original variance turned out to be shared by the parsimonious sets of indices in T1D and T2D. Starting from a pool of 25 indices assessed from CGM time series in T2D subjects, reduced subsets of metrics virtually providing the same information content can be determined by SPCA. The fact that these indices also appear in the parsimonious description of GV in T1D may indicate that they could be particularly informative of GV in diabetes, regardless of the specific type of disease.

In this paper, an effective algorithm for providing copyright protection is proposed by using a new embedding strategy for Discrete Wavelet Transform-based video watermarking. Discrete Wavelet Transform (DWT) is applied on the video, to convert the spatial data into frequency domain, having low pass and high pass components. The low frequency component is used for generating the key, by using the watermark image and the binarized Low frequency part (LL) of the video frame. Same procedure is applied on each frame to generate the key for corresponding frame. This generated key should be used at receiver for extracting the watermark which provides copyright protection. Blind watermarking technique is used in this paper which require only key to extract the embedded watermark. The original video is not required during extraction. To criticize the robustness of algorithm, the original watermark image is compared with extracted watermark image after several attacks and their Peak Signal to Noise Ratio (PSNR), Normalized Correlation Coefficient (NC) and Structural Similarity index (SSIM) are calculated. The experimental results demonstrate that the watermark is invisible and it is robust against the various attacks and addition of noise to the video.

Dual Bounds of Sparse Principal Component Analysis Sparse principal component analysis (PCA) is a widely used dimensionality reduction tool in machine learning and statistics. Compared with PCA, sparse PCA enhances the interpretability by incorporating a sparsity constraint. However, unlike PCA, conventional heuristics for sparse PCA cannot guarantee the qualities of obtained primal feasible solutions via associated dual bounds in a tractable fashion without underlying statistical assumptions. In “Using L1-Relaxation and Integer Programming to Obtain Dual Bounds for Sparse PCA,” Santanu S. Dey, Rahul Mazumder, and Guanyi Wang present a convex integer programming (IP) framework of sparse PCA to derive dual bounds. They show the worst-case results on the quality of the dual bounds provided by the convex IP. Moreover, the authors empirically illustrate that the proposed convex IP framework outperforms existing sparse PCA methods of finding dual bounds.

Quantization parameter based blind watermarking schemes are very popular. These schemes work in spatial domain as well as in various transform domains such as wavelet, discrete cosine, and real oriented wavelet. These schemes utilize one bit (binary) image as a watermark. However, in general, watermarks are any n bit image, where n ≥ 1. Therefore, these schemes are not suitable for the watermarks with n > 1. In this paper, we extend the existing work on blind watermarking schemes for n bits watermark images. The ‘division algorithm’ is used as a key component to develop the extended schemes. The proposed scheme is implemented in spatial domain. Experiments are done on one bit, two bits and eight bits watermark images. The results are investigated qualitatively by visualization, and measured quantitatively by peak-signal-to-noise-ratio (PSNR) and normalized correlation coefficient (NC). The results of the existing and proposed watermarking schemes are same for n = 1. The results of the proposed watermarking scheme are reliable for n > 1. A formula is proven for lower bound on PSNR, which is a key finding in the watermarking community. The formula is verified experimentally.

ABSTRACTThe implementation of conventional sparse principal component analysis (SPCA) on high-dimensional data sets has become a time consuming work. In this paper, a series of subspace projections are constructed efficiently by using Householder QR factorization. With the aid of these subspace projections, a fast deflation method, called SPCA-SP, is developed for SPCA. This method keeps a good tradeoff between various criteria, including sparsity, orthogonality, explained variance, balance of sparsity, and computational cost. Comparative experiments on the benchmark data sets confirm the effectiveness of the proposed method.

In telemedicine, digital watermarking is critical for safeguarding patient privacy by securely embedding sensitive Electronic Patient Records (EPR) directly into medical imagery. This work proposes a high-capacity, non-blind, and semi-fragile watermarking scheme leveraging a 64-bit complex-float computational pipeline and the Discrete Orthonormal Stockwell Transform (DOST) combined with Local Binary Pattern (LBP) features. To improve security, the architecture incorporates Arnold scrambling for confusion and chaotic mapping for diffusion-based encryption. In the attack-free scenario, the proposed method significantly outperformed the recent state-of-the-art (SOTA) technique across two MRI datasets. On the TCIA MRI dataset, the proposed method achieved mean values of 79.84 dB for Peak Signal-to-Noise Ratio (PSNR) and 0.9969 for Structural Similarity Index (SSIM), representing average improvements of 111.61% and 4.35%, respectively, over the SOTA baseline (37.73 dB and 0.9553). On the BMIBTD MRI dataset, the proposed method achieved mean values of 65.62 dB for PSNR and 0.9827 for SSIM, reflecting average improvements of 77.54% in PSNR and 11.14% in SSIM compared to the SOTA values (36.96 dB and 0.8842). Furthermore, the proposed method maintained a mean Normalised Correlation Coefficient (NCC) of 1, demonstrating performance parity with the SOTA technique in terms of general robustness. Extensive robustness analysis reveals a selective, tiered sensitivity profile: the scheme exhibits high resilience to stochastic channel noise (NCC≳0.90), a characteristic semi-fragile response to Median filtering (NCC≈0.6-0.8), and high sensitivity to deterministic manipulations such as JPEG compression, Mean filtering, and geometric transformations. Rather than a limitation, this tiered behaviour serves as an inherent integrity-authentication mechanism, where the degradation or destruction of the mantissa-embedded watermark provides immediate detection of unauthorised spatial tampering or resampling post-acquisition. Computational time analysis demonstrates that the proposed method maintains an efficient total mean execution time of 0.2203 seconds, with separate mean processing times of 0.1293 seconds for embedding and 0.0910 seconds for extraction, ensuring rapid performance for real-time clinical applications. The capacity (8.00 bpp) and efficiency (η = 0.0625) of the proposed technique were analysed and found to be high compared to recent SOTA methods. Security analysis confirms the proposed framework's robustness against statistical, differential, and brute-force attacks, achieving superior performance metrics-including high NPCR and information entropy-compared to recent SOTA methods. Beyond MRI datasets, the system demonstrated comparable performance when validated on X-ray and ultrasound scans, confirming the framework's cross-modality consistency. The proposed system provides a significant advancement in balancing high-fidelity diagnostic reconstruction with robust defence against cryptanalytic threats.

Video watermarking is an important method of protecting the intellectual property copyright of the video media. It allows embedding of copyright information into the video pictures. In this paper a new digital video watermarking scheme with an error correcting code (ECC) is proposed. This watermarking scheme maximizes the watermark payload while minimizing the perceptual degradation of video quality caused by the embedded watermark by means of an appropriate choice of embedding position. Three error correcting codes, BCH (31,8), turbo (3,1) and conv (2,1,3) were implemented and compared. We found that BCH (31,8) achieved higher error correcting capability than turbo (3,1) and conv (2,1,3) under the simulated noise tests

In this paper, we present a robust semi-blind video watermarking scheme in lifting wavelet transform (LWT) domain using Extreme Learning Machine (ELM). In this scheme, first the static video summary is generated using extraction of color features from video frames. Second, the frames comprised of video summary are watermarked in LWT domain. To develop a robust and real time watermarking scheme, a fast Single hidden Layer Feedforward Neural Network (SLFN) known as ELM is used for watermark embedding and extraction. To evaluate the performance of the present scheme, several signal processing attacks are applied to each watermarked frame. Experimental evidence shows that the proposed scheme is robust against selected attacks. Due to fastprocessing of frames, the proposed scheme is also found to be suitable for real time watermarking of video.