Digital Image Steganography Scheme Based on DWT and SVD

This paper presents digital watermarking for validating and protecting the integrity of digital Holy Quran in Portable Document Format (PDF). The need arises as minute alteration to text or content of Holy Scriptures could result in a devastating situation in the belief of the owners. By this research we have designed and evaluated a simple yet fragile and fidel image PDF digital watermarking technique using 2 and 4 level Discrete Wavelet Transform (DWT) in order to protect and detect the authenticity of a digital Holy Quran. A 512x512 PDF Quran was used as cover image and DWT carried out on it following the watermark image embedding process of a 128x128 and 32x32 on different trials. The Inverse DWT then carried out to obtain the watermarked Quran PDF. Imperceptibility test using Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) were used to evaluate the fidelity and fragility of the watermark scheme over four different embedding gain (k) of 0.001, 0.002, 0.003 and 0.004 for a more robust system. The experimental results demonstrated that our system was indeed effective.

Watermarking is crucial for protecting medical images, guaranteeing data integrity and authenticity in telemedicine, especially for rural healthcare facilities with limited computational power. This paper introduces a lightweight watermarking scheme that performs a comparative study of Lifting Wavelet Transform (LWT) versus Discrete Wavelet Transform (DWT), both combined with Discrete Cosine Transform (DCT), to achieve optimal imperceptibility, robustness, and computational efficiency. The comparison of LWT and DWT is essential because LWT has lower computational complexity and memory usage, hence more appropriate for resource-limited environments than DWT, and without the use of Singular Value Decomposition (SVD) to further reduce processing requirements and improve compatibility with low-power devices. The suggested approach adopts adaptive embedding against texture complexity, secure synchronization patterns with m-sequences and Barker codes, and geometric correction utilizing ORB for guaranteed watermark extraction under attacks from JPEG compression, Gaussian noise attack, rotation attack, median filter attack, and Gaussian blur. A new error correction code interleaved with majority voting is employed to increase the watermark’s strength. Tested at various embedding strengths, the scheme attains Peak Signal to Noise Ratio (PSNR) values over 40 dB and Structural Similarity Index (SSIM) values above 0.95, signifying extremely little visual degradation. Low Bit Error Rates (BER) and high Normalized Correlation (NC) in various attack scenarios validate its success. This computational-friendly solution is designed to arm remote healthcare organizations with limited computer ability to reliably authenticate and attest to medical images, meeting severe telemedicine demands.

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.

This paper presents a watermarking algorithm to embed robust watermark and fragile authentication data into color satellite images. The aim of this watermarking algorithm is to protect the copyright and image originality. This algorithm doesn't require the original image in the extraction process and it is a blind algorithm. The algorithm contains two watermarks, one for the copyright and another one for the image authenticity. The first watermark is embedded in the frequency domain using the discrete cosine transform while the second watermark is embedded in the spatial domain which consists of hash functions using MD5 and Hex formats. The fragile watermark is inserted in one row of each one of the RGB channels. The distortion caused by the embedding is assessed using peak signal to noise ratio (PSNR) and similarity structure index measure (SSIM). Robustness against different types of attacks have been assessed using Stirmark and by introducing small modifications to the satellite images. The robust watermarking scheme survived many attacks such as JPEG compression and filtering. The fragile watermark is very sensitive and any small modification to the image can be detected. It was found that embedding in the green channel offers better performance compared to red and blue channels. In addition the embedding in the Y channel offers better robustness against attacks.

Most Discrete Wavelet Transform(DWT)/Singular Value Decomposition(SVD) watermark methods are robust to common attacks, such as loss compression, noise and filtering attacks, but they cannot resist geometrical attacks. As Scale Invariant Feature Transform(SIFT) feature points are invariant to rotation, scaling and translation, a blind DWT-SVD watermark method against geometrical attacks based on SIFT is proposed. The square of the inscribed circle of the host image matrix is chosen as the watermark embedding area. Firstly, the watermark embedding area is decomposed with 1-level DWT and the low-frequency sub-band is divided into non-overlapping blocks; then the SVD is applied to every block. Secondly, the watermark is embedded by quantifying the maximum singular value of each block. Finally, SIFT feature points of the watermarked image are saved to detect and correct possible geometrical attacks. The experimental results show that the proposed method not only has the robustness to any angle of rotation attacks, wide range of scaling attacks and translation attacks, but also can achieve blind extraction.

The technique used to hide copyright information (watermark) into the digital audio signal is termed as audio watermarking. Audio watermarking is an excellent approach to provide a solution to mitigate challenges that occur from easy copying and distribution of audio files that are being downloaded or uploaded through the web. The audio watermarking algorithms proposed earlier were implemented by image or binary logo or a unique pattern as watermark. To tackle the difficulty of online musical piracy, unethical attempt to alter purposefully copyrighted audio data and unauthorized distribution and tampering of audio files an efficient audio watermarking method is suggested here. In this method the copyright information (watermark) which is an audio signal of shorter length is imperceptibly added into the original audio signal with greater duration. Here two audio watermarking methods using DCT (Discrete Cosine Transform) and then with DWT-SVD (Discrete Wavelet Transform — singular value decomposition) methods are proposed with audio signal encrypted with secure hash algorithm as watermark. A comparative study on performance evaluation of both DCT and DWT-SVD methods are made in terms of PSNR (Peak-Signal to Noise Ratio) and MSE (Mean Square Error). The results show that transparency is best for each methods and DWT-SVD method is more efficient against signal attacks. The elementary notion of this document is to provide a beneficial and satisfactory method for secure audio file transmission by providing copyright protection.

Video Steganography provides a secure way of communication by embedding the secret message inside video frames. Supported by a real-time environment, a significant growth in choosing video as a cover file has been noticed in the last decade. Large concealing capacity, more robustness to attacks, and indistinguishability of cover video and stego video are of major concern for any video steganography technique. In this paper, a novel technique has been proposed to achieve the above-mentioned objectives. Implementation of video steganography in the transform domain inherently contributes to improved robustness. We have embedded an image inside a chosen video file using the Lifting Scheme based on the Haar Wavelet Transform. To achieve increased concealing capacity, the Singular Value Decomposition technique has been applied before the actual embedding task. The proposed work is implemented using the MATLAB environment. The suggested method is then tested and analyzed for its robustness, capacity, and imperceptibility. It is concluded that proposed technique is robust against general video processing attacks, like low-pass filtering, scaling, rotation, transformation, and histogram equalization attacks. Additionally, the values for Peak Signal to Noise Ratio, Mean Square Error, Structure Similarity Index Matrix, and Correlation Coefficients were calculated and analyzed as a performance measure of the method.

Compression in image data needs to be done to manage storage space efficiently. In this study, image compression is performed using the Differential Evolution (DE) algorithm as an approach to achieve an optimal threshold for pixel clustering in images. The performance of this image compression method is evaluated based on the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) parameters. The PSNR of DE image compression ranges from 27 dB to 40 dB with threshold levels between 10 to 40. Meanwhile, the SSIM ranges from 0.83 to 0.97 at threshold levels of 10 to 40. The PSNR and SSIM of DE image compression are both at a good level, resulting in compressed image visuals that closely resemble the original image as the threshold levels increase.

The object of research is the adaptive switching weighted median image filter (ASWM) algorithm. This algorithm is one of the most effective in the field of impulse noise suppression. The computational complexity and algorithmic features of this adaptive nonlinear filter make it impossible to implement a filter that works in real time on modern PLD microcircuits. The most problematic areas of the algorithm are the weight coefficient estimation cycle, which has no limit on the number of iterations and contains a large number of division operations. This does not allow implementing the filter on PLDs with a sufficiently effective method. In the course of the research, the programming model of the filter in Python was used. The performance of the algorithm was assessed using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics. Modeling made it possible to find out empirically the number of iterations of the cycle for estimating the weight coefficients at different levels of noise density and to estimate the effect of artificial limitation of the maximum number of iterations on the filter performance. Regardless of the intensity of the noise impact, the algorithm performs less than 40 iterations of the evaluation cycle. Let’s also simulate the operation of the algorithm with different variants of the division module implementation. The paper considers the main of them and offers the most optimal in terms of the ratio of accuracy/hardware costs for implementation. Thus, a modified algorithm was proposed that does not have these disadvantages. Thanks to modifications of the algorithm, it is possible to implement a pipelined ASWM image filter on modern PLDs. The filter is synthesized for the main families of Intel PLDs. The implementation, which is not inferior in terms of SSIM and PSNR metrics to the original algorithm, requires less than 65,000 FPGA logical cells and allows filtering of monochrome images with FullHD resolution at 48 frames/s at a clock frequency of 100 MHz.

This paper deals with the application of Spatial and Temporal DWT (Discrete Wavelet transform) on the videos. Here we will discuss about three mechanisms and their performance on videos at increased DWT level. In any video processing algorithm, memory is the major criteria. In these three mechanisms dynamic (automatic) DWT level selection and manual level selection is implemented. Here we will also discuss about implementation of different DWT level in spatial and temporal domain. In this paper Haar wavelet is taken as the reference as it has its inherent properties and ease of implementation.

This paper proposes the enhancement of low contrast satellite images based on intensity transformation. The intensity transformation is achieved using gamma correction on discrete wavelet transform (DWT) and the better illumination is achieved by singular value decomposition (SVD) technique. The DWT decomposes the input image in four different frequency sub-bands; low-low (LL), low-high (LH), high-low (HL) and high-high (HH). The gamma correction is applied only on LL sub-band to preserve the edge information. To improve the illumination, these gamma corrected LL sub-band information are passed through SVD. The enhanced image obtained is reconstructed by taking inverse discrete wavelet transform (IDWT). The performance measure is carried out in terms of EME, entropy and signals to noise ratio.

An adaptive optimum weighted mean filter and bilateral filter for noise removal in cardiac MRI images

WITHDRAWN: Contrasting the performance metrics of discrete transformations on digital image steganography using artificial intelligence

In this study, we propose a CNN-GAN-based real-time processing technique for filtering images of underwater cables used in power systems. This addresses the excessive interference impurities that are frequently observed in images captured by remotely operated vehicles (ROVs). The process begins with the input of the original image into the convolutional neural network (CNN). Subsequently, the training outcomes, which serve as input parameters for the generative adversarial network (GAN), facilitate the filtering process. The system also calculates both the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR), performing model updates via backward propagation. This technique utilizes deep learning technologies to achieve rapid, real-time filtering of underwater cable images. The experimental results reveal that the loss function of the CNN reaches 0.16 with an accuracy of 97.5%, while the loss function of the adversarial GAN network approaches 0.05. Compared with traditional methods such as DDN, JORDOR, RESCAN, and PRENet, the proposed CNN-GAN algorithm exhibits superior performance, as evidenced by the higher PSNR and SSIM values. Specifically, for clear water images, the PSNR reaches 29.86 dB and the SSIM is 0.9045. For severely polluted images, the PSNR is 28.67 dB and the SSIM is 0.8965, while for unevenly illuminated images, the PSNR and SSIM values are 24.37 dB and 0.88, respectively. These enhancements significantly benefit the monitoring and maintenance of power systems.

We use images in several important areas such as military, health, security, and science. Images can be distorted during the capturing, recording, processing, and storing. Image quality metrics are the techniques to measure the quality and quality accuracy level of the images and videos. Most of the quality measurement algorithms does not affect by small distortions in the image. Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Ultrasonic Imaging (UI) are widely used in the health sector. Because of several reasons it might be artifacts in the medical images. Doctor decisions might be affected by these image artifacts. Image quality measurement is an important and challenging area to work on. There are several metrics that have been done in the literature such as mean square error, peak signal-noise ratio, gradient similarity measure, structural similarity index, and universal image quality. Patient information can be an embedded corner of the medical image as a watermark. Watermark can be considered one of the image distortions types. The most common objective evaluation algorithms are simple pixel based which are very unreliable, resulting in poor correlation with the human visual system. In this work, we proposed a new image quality metric which is a Measure of Singular Value Decomposition (M-SVD). Experimental results show that novel M-SVD algorithm gives very promising results against Peak Signal to Noise Ratio (PSNR), the Mean Square Error (MSE), Structural Similarity Index Measures (SSIM), and 3.4. Universal Image Quality (UIQ) assessments in watermarked and distorted images such as histogram equalization, JPEG compression, Gamma Correction, Gaussian Noise, Image Denoising, and Contrast Change.