Graph-based Transformation Research Articles

M-Sequences and Sliding Window Based Audio Watermarking Robust Against Large-Scale Cropping Attacks

Large-scale cropping (LSC) is one of the mostly-used operations in desynchronization attacks and can easily destroy the watermark information by deleting continuous audio slices from the watermarked audio. In this work, we propose a spread spectrum (SS) based audio watermarking scheme to resist against LSC attacks more robustly from both theoretical and empirical perspectives. Specifically, we first perform discrete wavelet transform (DWT), graph-based transform (GBT), and singular value decomposition (SVD) on the host audio signal to produce transform coefficients. Next, we embed the chaotic encrypted watermark into DWT-GBT-SVD coefficients by the SS technique. Then, we combine m-sequences with the encrypted watermark to generate the watermarking key, which can theoretically guarantee the self-restoration of the cropped watermark based on the periodicity of m-sequences. Additionally, we develop an effective sliding window (SW) strategy to extract the fragmentary watermark slices from DWT-GBT-SVD coefficients and restore the integral watermark by the watermarking key. Finally, the proposed audio watermarking scheme, named m-SW-LSC, is compared with the state-of-the-art audio watermarking methods on audio signals with different genres and lengths under various attacks with different ratios. Experimental results demonstrate that our m-SW-LSC has a superior performance in restoring the complete watermark and exhibits significantly high robustness against LSC attacks.

Dual-color blind image watermarking algorithm using the graph-based transform in the stationary wavelet transform domain

Aiming at the problem of color image copyright protection, a dual-color blind image watermarking algorithm using the graph-based transform (GBT) in the stationary wavelet transform domain (SWT) is proposed. First, the color watermark image and the color carrier image are separated into their R, G, and B channel components. Second, the R, G, and B components of the color watermark are scrambled by the Fibonacci–Lucas transform and encrypted with the cosine-transform-based chaotic system (CTBCS). Each component of the encrypted watermark is then converted into a binary sequence. Third, the low-frequency sub-bands from each component of the carrier image performed by the SWT are divided into non-overlapping blocks. Finally, the three binary watermark sequences are embedded into the GBT low-frequency coefficients of selected image blocks of the corresponding component by the parity quantization. Before the watermark extraction, the image to be authenticated is rotationally corrected based on the Fan-beam transform. This operation improves the resistance of the proposed algorithm to rotation geometry attacks. Experimental results show that the algorithm is robust to conventional signal processing attacks and to arbitrary angle rotation attacks while effectively ensuring the invisibility of the watermark.

A Novel Optimized Graph-Based Transform Watermarking Technique to Address Security Issues in Real-Time Application

The multimedia technologies are gaining a lot of popularity these days. Many unauthorized persons are gaining the access of multimedia such as videos, audios, and images. The transmission of multimedia across the Internet by unauthorized person has led to the problem of illegal distribution. The problem arises when copyrighted data is getting accessed without the knowledge of copyright owner. The videos are the most attacked data during COVID-19 pandemic. In this paper, the frame selection video watermarking technique is proposed to tackle the issue. The proposed work enlightens frame selection followed by watermarking embedding and testing of the technique against various attacks. The embedding of the watermark is done on selected frames of the video. The additional security feature Hyperchaotic Encryption is applied on watermark before embedding. Watermark embedding is done using graph-based transform and singular-valued decomposition and the performance of the technique is further optimized using hybrid combination of grey wolf optimization and genetic algorithm. Many researchers face the challenge of quality loss after embedding of watermark. Proposed technique will aim to overcome those challenges. A total of 6 videos (Akiyo, Coastguard, Foreman, News, Bowing, and Pure Storage) are used for carrying out research work. The performance evaluation of the proposed technique has been carried out after processing it against practical video processing attacks Gaussian noise, sharpening, rotation, blurring, and JPEG compression.

A robust Plenoptic image watermarking method using graph-based transform

Numerous methods have been introduced for digital images watermarking. But there are fewer studies on plenoptic images. This image has more information (such as depth) and costs more than regular digital images. So, protecting the ownership of plenoptic is important. In this paper a robust plenoptic image watermarking has been introduced which is based on Graph-based Transform (GBT). Also, in order to maximizing the robustness of watermarking an optimization has been used to find the best graph structure of GBT. This optimization has done using a Genetic Algorithm. One of the common ways to robust the watermarking is the use of discrete cosine transform (DCT). In this study, we have shown that the proposed method is much more powerful than DCT. The proposed method is tested on three plenoptic images namely Alley, Basic Shapes and Coffee Table and the results show that the proposed method is more robust to similar methods such as DCT methods. Watermarking was evaluated using BER, SSIM and PSNR criteria and Gaussian noise attacks with varying intensities.

Graph-based Transforms for Video Coding.

In many state-of-the-art compression systems, signal transformation is an integral part of the encoding and decoding process, where transforms provide compact representations for the signals of interest. This paper introduces a class of transforms called graph-based transforms (GBTs) for video compression, and proposes two different techniques to design GBTs. In the first technique, we formulate an optimization problem to learn graphs from data and provide solutions for optimal separable and nonseparable GBT designs, called GL-GBTs. The optimality of the proposed GL-GBTs is also theoretically analyzed based on Gaussian-Markov random field (GMRF) models for intra and inter predicted block signals. The second technique develops edge-adaptive GBTs (EA-GBTs) in order to flexibly adapt transforms to block signals with image edges (discontinuities). The advantages of EA-GBTs are both theoretically and empirically demonstrated. Our experimental results show that the proposed transforms can significantly outperform the traditional Karhunen-Loeve transform (KLT).

MCLEAN: Multilevel Clustering Exploration As Network.

Finding useful patterns in datasets has attracted considerable interest in the field of visual analytics. One of the most common tasks is the identification and representation of clusters. However, this is non-trivial in heterogeneous datasets since the data needs to be analyzed from different perspectives. Indeed, highly variable patterns may mask underlying trends in the dataset. Dendrograms are graphical representations resulting from agglomerative hierarchical clustering and provide a framework for viewing the clustering at different levels of detail. However, dendrograms become cluttered when the dataset gets large, and the single cut of the dendrogram to demarcate different clusters can be insufficient in heterogeneous datasets. In this work, we propose a visual analytics methodology called MCLEAN that offers a general approach for guiding the user through the exploration and detection of clusters. Powered by a graph-based transformation of the relational data, it supports a scalable environment for representation of heterogeneous datasets by changing the spatialization. We thereby combine multilevel representations of the clustered dataset with community finding algorithms. Our approach entails displaying the results of the heuristics to users, providing a setting from which to start the exploration and data analysis. To evaluate our proposed approach, we conduct a qualitative user study, where participants are asked to explore a heterogeneous dataset, comparing the results obtained by MCLEAN with the dendrogram. These qualitative results reveal that MCLEAN is an effective way of aiding users in the detection of clusters in heterogeneous datasets. The proposed methodology is implemented in an R package available at https://bitbucket.org/vda-lab/mclean.

Graph-Based Transform for 2D Piecewise Smooth Signals With Random Discontinuity Locations.

The graph-based block transform recently emerged as an effective tool for compressing some special signals such as depth images in 3D videos. However, in existing methods, overheads are required to describe the graph of the block, from which the decoder has to calculate the transform via time-consuming eigendecomposition. To address these problems, in this paper, we aim to develop a single graph-based transform for a class of 2D piecewise smooth signals with similar discontinuity patterns. We first consider the deterministic case with a known discontinuity location in each row. We propose a 2D first-order autoregression (2D AR1) model and a 2D graph for this type of signals. We show that the closed-form expression of the inverse of a biased Laplacian matrix of the proposed 2D graph is exactly the covariance matrix of the proposed 2D AR1 model. Therefore, the optimal transform for the signal are the eigenvectors of the proposed graph Laplacian. Next, we show that similar results hold in the random case, where the locations of the discontinuities in different rows are randomly distributed within a confined region, and we derive the closed-form expression of the corresponding optimal 2D graph Laplacian. The theory developed in this paper can be used to design both pre-computed transforms and signal-dependent transforms with low complexities. Finally, depth image coding experiments demonstrate that our methods can achieve similar performance to the state-of-the-art method, but our complexity is much lower.

Research on Software Architecture Modeling and Graph-based Transformation

Research on Software Architecture Modeling and Graph-based Transformation

GReTL: an extensible, operational, graph-based transformation language

This article introduces the graph-based transformation language GReTL. GReTL is operational, and transformations are either specified in plain Java using the GReTL API or in a simple domain-specific language. GReTL follows the conception of incrementally constructing the target metamodel together with the target graph. When creating a new metamodel element, a set-based semantic expression is specified that describes the set of instances that have to be created in the target graph. This expression is defined as a query on the source graph. GReTL is a kernel language consisting of a minimal set of operations, but it is designed for being extensible. Custom higher-level operations can be built on top of the kernel operations easily. After a description of the foundations of GReTL, its most important elements are introduced along with a transformation example in the field of metamodel integration. Insights into the design of the GReTL API are given, and a convenience copy operation is implemented to demonstrate GReTL's extensibility.

Aspect-oriented model-driven skeleton code generation: A graph-based transformation approach

Model-driven code generation has been investigated in traditional and object-oriented design paradigms; significant progress has been made. It offers many advantages including the rapid development of high quality code. Errors are reduced and the consistency between the design and the code is retained, in comparison with a purely manual approach. Here, a model-driven code generation approach based on graph transformations for aspect-oriented development is proposed. The approach has two main transformation activities. The first activity transforms a visual (graphical) model of the design into a formal, text-based notation that can be readily processed. The graphical model is created by the software designer and uses a UML profile for aspect-oriented software (i.e., FDAF) to represent aspects and their components. XML is the target notation for this step; the transformation uses the XML meta-model to ensure that the output complies with the language. The second activity transforms the XML model into AspectJ source code. The transformation uses the AspectJ meta-model to ensure the output complies with the language. The transformations from the extended UML model to XML and from XML to AspectJ code are fully automated. The transformation algorithms are based on graph transformations; tool support has been developed. Key technical issues in the approach are discussed, including performance, the amount of code generated, correctness, and adaptability, in addition to a comparison of the proposal with existing alternative approaches. The approach has been validated on three example systems: a banking system, classroom scheduling system, and an insurance system. The banking system example is presented in the paper.

Operational semantics for Petri net components

We propose a calculus for marked labelled nets (components), with places and transitions as atoms and merge, addition, fusion and relabelling as operators. The operators are defined using graph-based transformations; each net can be represented by a term. Next, we define both a step semantics for nets and a Plotkin-style SOS semantics for net terms and show their equivalence. In the semantics, both state-oriented and event-oriented properties of components can be expressed. We give a few rules for reducing components to smaller equivalent ones.