Dual Image Watermarking Scheme based on DWT-SVD

The active development of internet, advance smart hand held devices and multimedia technologies have made it possible to easily create, copy, transmit, and distribute multimedia data instantly. Besides all of these advantages, there are many undesired issues including the piracy of digital data. It creates as issue like protection rights of the content and ownership. This concern has drawn the attention of the researchers toward the development of multimedia protection schemes using digital watermark. In this paper, a new image watermarking algorithm is presented which is robust against various attacks. DWT and SVD have been used to embed two watermarks in the HL and LH bands of the host image. Simulation evaluation demonstrates that the proposed technique withstand various attacks

Image Reconstruction Using Modified Hybrid Transform

In this paper, an algorithm for reconstruction of a completely lost blocks using Modified Hybrid Transform. The algorithms examined in this paper do not require a DC estimation method or interpolation. The reconstruction achieved using matrix manipulation based on Modified Hybrid transform. Also adopted in this paper smart matrix (Detection Matrix) to detect the missing blocks for the purpose of rebuilding it. We further asses the performance of the Modified Hybrid Transform in lost block reconstruction application. Also this paper discusses the effect of using multiwavelet and 3D Radon in lost block reconstruction

Audio, Text, Image, and Video Digital Watermarking Techniques for Security of Media Digital

The proliferation of multimedia content as digital media assets, encompassing audio, text, images, and video, has led to increased risks of unauthorized usage and copyright infringement. Online piracy serves as a prominent example of such misuse. To address these challenges, watermarking techniques have been developed to protect the copyright of digital media while maintaining the integrity of the underlying content. Key characteristics evaluated in watermarking methods include capability, privacy, toughness, and invisibility, with robustness playing a crucial role. This paper presents a comparative analysis of digital watermarking methods, highlighting the superior security and effective watermark image recovery offered by singular value decomposition. The research community has shown significant interest in watermarking, resulting in the development of various methods in both the spatial and transform domains. Transform domain approaches such as Discrete Cosine Transform, Discrete Wavelet Transform, and Singular Value Decomposition, along with their interconnections, have been explored to enhance the effectiveness of digital watermarking methods

Graph Spectral Image Processing

Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structure, then one can interpret the image (or image patch) as a signal on a graph, and apply GSP tools for processing and analysis of the signal in graph spectral domain. In this article, we overview recent graph spectral techniques in GSP specifically for image / video processing. The topics covered include image compression, image restoration, image filtering and image segmentation

Performance Enhancement of Time Delay and Convolutional Neural Networks Employing Sparse Representation in the Transform Domains

Deep neural networks are quickly advancing and increasingly used in many applications; however, these networks are often extremely large and require computing and storage power beyond what is available in most embedded and sensor devices. For example, IoT (Internet of Things) devices lack powerful processors or graphical processing units (GPUs) that are commonly used in deep networks. Given the very large-scale deployment of such low power devices, it is desirable to design methods for efficient reduction of computational needs of neural networks. This can be done by reducing input data size or network sizes. Expectedly, such reduction comes at the cost of degraded performance. In this work, we examine how sparsifying the input to a neural network can significantly improve the performance of artificial neural networks (ANN) such as time delay neural networks (TDNN) and convolutional neural networks (CNNs). We show how TDNNs can be enhanced using a sparsifying transform layer that significantly improves learning time and forecasting performance for time series. We mathematically prove that the improvement is the result of sparsification of the input of a fully connected layer of a TDNN. Experiments with several datasets and transforms such as discrete cosine transform (DCT), discrete wavelet transform (DWT) and PCA (Principal Component Analysis) are used to show the improvement and the reason behind it. We also show that this improved performance can be traded off for network size reduction of a TDNN. Similarly, we show that the performance of reduced size CNNs can be improved for image classification when domain transforms are employed in the input. The improvement in CNN performance is found to be related to the better preservation of information when sparsifying transforms are used. We evaluate the proposed concept with low complexity CNNs and common datasets of Fashion MNIST and CIFAR. We constrain the size of CNNs in our tests to under 200K learnable parameters, as opposed to millions in deeper networks. We emphasize that finding optimal hyper parameters or network configurations is not the objective of this study; rather, we focus on studying the impact of projecting data to new domains on the performance of reduced size inputs and networks. It is shown that input size reduction of up to 75% is possible, without loss of classification accuracy in some cases

Image Compression and Watermarking scheme using Scalar Quantization

This paper presents a new compression technique and image watermarking algorithm based on Contourlet Transform (CT). For image compression, an energy based quantization is used. Scalar quantization is explored for image watermarking. Double filter bank structure is used in CT. The Laplacian Pyramid (LP) is used to capture the point discontinuities, and then followed by a Directional Filter Bank (DFB) to link point discontinuities. The coefficients of down sampled low pass version of LP decomposed image are re-ordered in a pre-determined manner and prediction algorithm is used to reduce entropy (bits/pixel). In addition, the coefficients of CT are quantized based on the energy in the particular band. The superiority of proposed algorithm to JPEG is observed in terms of reduced blocking artifacts. The results are also compared with wavelet transform (WT). Superiority of CT to WT is observed when the image contains more contours. The watermark image is embedded in the low pass image of contourlet decomposition. The watermark can be extracted with minimum error. In terms of PSNR, the visual quality of the watermarked image is exceptional. The proposed algorithm is robust to many image attacks and suitable for copyright protection applications.Comment: 11 Pages, IJNGN Journal 201