PCA-DTT 2D-DHCES based attack resilient imperceptible image watermarking

An enhanced hybrid image watermarking scheme for security of medical and non-medical images based on DWT and 2-D SVD

Low-rank prior-based Fast-RPCA for clutter filtering and noise suppression in non-contrast ultrasound microvascular imaging

Background/foreground separation is one of the most fundamental tasks in computer vision, especially for video data. Robust PCA (RPCA) and its tensor extension, namely, Robust Tensor PCA (RTPCA), provide an effective framework for background/foreground separation by decomposing the data into low-rank and sparse components, which contain the background and the foreground (moving objects), respectively. However, in real-world applications, the video data is contaminated with noise. For example, in metal additive manufacturing (AM), the processed X-ray video to study melt pool dynamics is very noisy. RPCA and RTPCA are not able to separate the background, foreground, and noise simultaneously. As a result, the noise will contaminate the background or the foreground or both. There is a need to remove the noise from the background and foreground. To achieve the three components decomposition, a smooth sparse Robust Tensor Decomposition (SS-RTD) model is proposed to decompose the data into static background, smooth foreground, and noise, respectively. Specifically, the static background is modeled by the low-rank tucker decomposition, the smooth foreground (moving objects) is modeled by the spatio-temporal continuity, which is enforced by the total variation regularization, and the noise is modeled by the sparsity, which is enforced by the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> norm. An efficient algorithm based on alternating direction method of multipliers (ADMM) is implemented to solve the proposed model. Extensive experiments on both simulated and real data demonstrate that the proposed method significantly outperforms the state-of-the-art approaches for background/foreground separation in noisy cases. Note to Practitioners—This work is motivated by melt pool detection in metal additive manufacturing where the processed X-ray video from the monitoring system is very noisy. The objective is to recover the background with porosity defects and the foreground with melt pool in the presence of noise. Existing methods fail to separate the noise from the background and foreground since RPCA and RTPCA have only two components, which cannot explain the three components in the data. This paper puts forward a smooth sparse Robust Tensor Decomposition by decomposing the tensor data into low-rank, smooth, and sparse components, respectively. It is a highly effective method for background/foreground separation in noisy case. In the case studies on simulated video and X-ray data, the proposed method can handle non-additive noise, and even the case of high noise-ratio. In the proposed algorithm, there is only one tuning parameter <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> . Based on the case studies, our method achieves satisfying performance by taking any <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\lambda \in [{0.2,1}]$ </tex-math></inline-formula> with anisotropic total variation regularization. With this observation, practitioners can apply the proposed method without extensive parameter tuning work. Furthermore, the proposed method is also applicable to other popular industrial applications. Practitioners can use the proposed SS-RTD for degradation processes monitoring, where the degradation image contains the static background, anomaly, and random disturbance, respectively.

Digital watermarking is a process to provide authenticity by hiding a data into an image or audio or document. Hiding of data in an image can be done in frequency domain. Since frequency domain based techniques are more robust against signal processing and geometric attacks than time domain based techniques and watermark can also be extracted with(non blind) and without(blind) original cover image. In this paper we proposed a blind image watermarking technique which embeds watermark into image in frequency domain using discrete wavelet transform, singular value decomposition and torus automorphism techniques. This technique extracts watermark without cover image and also proved that this method is robust against different signal and non signal processing attacks.

In this paper, an image watermarking scheme is presented, in which Least Significant Bit and discrete cosine transform are used. The discrete cosine transform is performed on the original host image, and the secret watermark image is embedded into the coefficient of discrete cosine transform, which will replace the least significant bit. The embedded secret watermark bit will cause minimal distortion of the original host image, but we cannot find the difference of the original host image and the watermarked image. The experiment based on this algorithm demonstrates that the watermarking is robust to the common signal processing techniques, including noise attack, JPEG Compression attack and so on.

Steganography is a method of hiding sensitive information within an ordinary file to avoid detection. When the file is received, the hidden information is extracted from the original file. Various types of files can be used to conceal the information, including images, audio files, video files, and text files. This research paper focuses on the use of image files as the medium. To create a stego-image, a secret image is embedded into a cover image. Both the cover and secret images are subjected to a Daubechies 3 Discrete Wavelet Transformation (DWT), then encrypted using Arnold's Cat Map algorithm and fused using a simple alpha blending technique with the cover image. DWT enhances the invisibility of hidden information, making it more challenging to detect by visual inspection or statistical analysis. Arnold’s cat map adds an extra layer of security, making it more difficult for an attacker to extract the message. Alpha blending helps to maintain the visual quality and integrity of the cover image, reducing the chances of noticeable artifacts or anomalies that may arise due to the embedding process. The resulting stego-image appears visually like the original cover image, making it less suspicious to potential attackers. Experimental results have been made on multiple area such as the best value of alpha, which DWT family to use, and different combinations of images as cover and payload images. The results demonstrate that our proposed hybrid model achieves a high level of security, robustness, and imperceptibility in image steganography. Moreover, it outperforms existing methods in terms of image quality metrics, such as achieving a Peak Signal-to-Noise Ratio (PSNR) value of up to 70.8 and a Structural Similarity Index (SSIM) value of up to 0.98982. These metrics indicate that the stego-image retains a remarkable level of visual fidelity and similarity to the cover image, thereby enhancing the overall effectiveness of the steganography technique.

In recent years, with the development of the information society, digital images can be easily uploaded and downloaded. Against this background, instances of plagiarism and tampering with images have been increasing. Therefore, it is necessary to develop robust watermarking for the copyright protection of digital images. In this study, we propose a blind digital image watermarking method based on dual-tree complex discrete wavelet transform and quantization. We apply this wavelet transform to the original image and embed two watermarked images of different sizes using quantization index modulation and relative modulation. Quantization index modulation is robust against geometric attacks. Relative modulation is robust against noise addition. The results of our experiments show that our method is robust against Gaussian noise, salt and pepper noise, cropping, and Jpeg compression. The experimental results show that the watermark can be extracted by Gaussian noise, salt and pepper noise, cropping, Jpeg compression. In addition, we visually check the extracted watermarked images. Furthermore, we compare three methods: one using dual-tree complex discrete wavelet transform and sliding window. The second method is based on discrete wavelet transform, discrete cosine transform, and singular value decomposition. The third method is based on Discrete Cosine Transform and RM, QIM. By comparing these three methods with the existing methods, we confirmed the significance and performance of our method.

In this paper, a novel image watermarking scheme is proposed, based on Contourlet Transform (CT) and Matrix Factorization methods namely, Schur Factorization and Singular Value Decomposition (SVD). The original image and watermark are both decomposed by CT. The lowest frequency coefficients of original image after CT are factorized using SVD and that of watermark after CT are decomposed by Schur Factorization. The schur form of watermark is further factorized using SVD. The modified coefficients of singular values of watermark are embedded in the singular values of original image followed by inverse SVD and inverse CT computation that results in the watermarked image. The experimental results demonstrate that the scheme is resistant against signal processing attacks such as median filter, salt and pepper noise, Gaussian noise, compression and geometric attacks.KeywordsDigital watermarkingContourlet TransformSchur factorizationSVD factorization

The growth of internet coupled with the rise in networked infrastructure has resulted in exponential increase in the multimedia content being shared over the communication networks. The advancement in technology has resulted in increase in multimedia piracy. This is due to the fact that it is very easy to copy, duplicate and distributes multimedia content using current day technology. In such a scenario Digital Rights Management is one of the prominent issues to be dealt with and tremendous work is going on in this direction round the globe. Digital watermarking and fingerprinting have emerged as fundamental technologies to cater to DRM issues. These technologies have been found to be of prominent use in content authentication, copy protection, copyright control, broadcast monitoring and forensic applications. Various requirements of a digital watermarking system include Imperceptibility, Robustness, Security, Payload and Computational complexity. The main requirement of real time DRM systems is lesser computational complexity and high robustness. This chapter proposes and analyses a robust and computational efficient Image watermarking technique in spatial domain based on Inter Block Pixel Difference (IBPD). The cover image is divided into 8 × 8 non overlapping blocks and difference between intensities of two pixels of adjacent blocks at predefined positions is calculated. Depending upon the watermark bit to be embedded; both the pixels are modified to bring the difference in a predefined zone. The experimental results reveal that the proposed scheme is capable of providing high quality watermarked images in addition to being robust to various singular and hybrid image processing and geometrical attacks like Salt and Pepper noise, Gaussian noise, Sharpening, Compression, Rotation and Cropping etc. Further the implementation of the scheme in pixel domain reduces the computational complexity drastically and makes the proposed scheme an ideal candidate for real time applications.

Moving object detection is a fundamental step in various computer vision applications. Robust Principal Component Analysis (RPCA) based methods have often been employed for this task. However, the performance of these methods deteriorates in the presence of dynamic background scenes, camera jitter, camouflaged moving objects, and/or variations in illumination. It is because of an underlying assumption that the elements in the sparse component are mutually independent, and thus the spatiotemporal structure of the moving objects is lost. To address this issue, we propose a spatiotemporal structured sparse RPCA algorithm for moving objects detection, where we impose spatial and temporal regularization on the sparse component in the form of graph Laplacians. Each Laplacian corresponds to a multi-feature graph constructed over superpixels in the input matrix. We enforce the sparse component to act as eigenvectors of the spatial and temporal graph Laplacians while minimizing the RPCA objective function. These constraints incorporate a spatiotemporal subspace structure within the sparse component. Thus, we obtain a novel objective function for separating moving objects in the presence of complex backgrounds. The proposed objective function is solved using a linearized alternating direction method of multipliers based batch optimization. Moreover, we also propose an online optimization algorithm for real-time applications. We evaluated both the batch and online solutions using six publicly available datasets that included most of the aforementioned challenges. Our experiments demonstrated the superior performance of the proposed algorithms compared with the current state-of-the-art methods.

RPCA (Robust Principal Component Analysis) recovers sparse and low rank components from the original observation data, RPCA commonly uses ADM (Alternate Direction Method) for solving, the efficiency of algorithm depends on the nuclear norm optimization solution, that is SVD. And the application of RPCA in computer vision, image and video and so on, large amounts of data, such as images and video, make it difficult for large-scale data SVD. In this paper, we used the random matrix algorithm to improve the SVD, respectively the Count Sketch algorithm, the Prototype Randomized k-SVD algorithm and the Faster Randomized k-SVD algorithm, The main idea is to reduce the size of the original large-scale data matrix and sample randomly. Using the random projection algorithm to obtain an approximation of the original matrix, and operate QR decomposition of the approximate matrix, get the unitary matrix corresponding to the approximate matrix, and do the corresponding SVD, finally we can get the results which is similar to the original matrix calculation. Obtaining approximation of the original data matrix. But the cost time and space have been greatly optimized. Simulation experiments based on single image and video foreground detection show that the proposed methods can greatly improve the efficiency of RPCA iterative optimization.

Geometric attacks are the Achilles heel for many image watermarking schemes. Geometric attacks can be decomposed into two classes: global affine transforms and local geometrical distortions. Most countermeasures proposed in the literature only address the problem of global affine transforms (e.g. rotation, scaling and translation). In this paper, we propose a blind image watermarking algorithm robust to local geometrical distortions such as row or column removal, cropping, local random bend, etc. The robust feature points are adaptively extracted from digital images and local image regions (circular regions) that are invariant to geometric attacks are obtained according to the multi-scale space representation and image normalization. At each local image region, the watermark is embedded by quantizing the magnitudes of the pseudo-Zernike moments. By binding digital watermark with local image regions, resilience against local geometrical distortions can be readily obtained. Experimental results show that the proposed image watermarking is not only invisible and robust against common image processing operations, such as sharpening, noise adding, JPEG compression, etc, but also robust against geometric attacks such as rotation, translation, scaling, row or column removal, copping, local random bend, etc.

Cellular automata based color image watermarking using non sub sampled contourlet transform (NSCT) and singular value decomposition (SVD) is presented in this paper. Game of life (GOL) cellular automata is used to scramble binary watermark so that no one can recover the watermark without secret scrambling keys. Because of the salient features like multiscale, multidirection and shift invariance of NSCT, 2-level NSCT is applied to red, green and blue component of RGB cover image. Most of the perceptual content of an image focuses on low frequency subband of NSCT and the singular values taken from low frequency subband have certain stability. So a modest change in the singular values taken from low frequency subband of NSCT is applicable. In this proposed scheme, each singular value of scrambled binary watermark is embedded in fixed location of singular value matrix of low frequency subbands of NSCT of red, green and blue components. The scheme offers security, imperceptibility and robustness against various image processing and geometric attacks like Gaussian white noise, salt and pepper noise, median filtering, JPEG compression, cropping and rotation. The proposed scheme is compared with existing watermarking schemes in terms of Peak Signal to Noise ratio (PSNR), Normalized Correlation (NC)and provides satisfactory performance than other schemes.

Digital subtraction angiography (DSA) is one of imaging methods using X-ray image for clear visualization of the vessel information during intervention with a catheter. In order to obtain a fine DSA image, patients have to hold their breathing. However, steady breath hold is a burden for the patients and is sometimes difficult for elder patients. We propose a blood vessel enhancement method with consecutive digital angiographic images acquired under the natural breathing. Robust principal component analysis (RPCA) is used to enhance blood vessel information from consecutive angiographic images acquired under the natural breathing. RPCA can separate the consecutive images into a low-rank component and a sparse component. The information of contrast media is included in the sparse component. We implemented it on GPU and applied the proposed method to 13 sets of angiographic images and confirmed that it enables to generate satisfactory enhanced angiographic images.

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.