Three-level Discrete Wavelet Transform Research Articles

An Image Encryption Algorithm Based on Improved Hilbert Curve Scrambling and Dynamic DNA Coding.

As an effective method for image security protection, image encryption is widely used in data hiding and content protection. This paper proposes an image encryption algorithm based on an improved Hilbert curve with DNA coding. Firstly, the discrete wavelet transform (DWT) decomposes the plaintext image by three-level DWT to obtain the high-frequency and low-frequency components. Secondly, different modes of the Hilbert curve are selected to scramble the high-frequency and low-frequency components. Then, the high-frequency and low-frequency components are reconstructed separately using the inverse discrete wavelet transform (IDWT). Then, the bit matrix of the image pixels is scrambled, changing the pixel value while changing the pixel position and weakening the strong correlation between adjacent pixels to a more significant correlation. Finally, combining dynamic DNA coding and ciphertext feedback to diffuse the pixel values improves the encryption effect. The encryption algorithm performs the scrambling and diffusion in alternating transformations of space, frequency, and spatial domains, breaking the limitations of conventional scrambling. The experimental simulation results and security analysis show that the encryption algorithm can effectively resist statistical attacks and differential attacks with good security and robustness.

A passive approach for the detection of splicing forgery in digital images

With the technology progress, a plethora of freely accessible software has questioned the authenticity of digital images. This field is continuously creating challenges for researchers to ascertain the integrity of images. Hence, there is a need to improve the performance of forgery detection algorithms from time to time. This paper is focused on the detection of splicing forgery because it is one of the most frequently used image manipulation techniques. In the proposed scheme, Markov features in both Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP) domains are extracted and combined for the detection of image splicing. Three-level DWT is applied to the source image by the means of discrete Haar wavelet. The image is split in to high and low-frequency sub-bands after applying one level DWT. Furthermore, low-frequency sub-band is decomposed twice to obtain three-level DWT, which leads to more information and less amount of noise. The efficacy of the proposed scheme has been appraised on six benchmark datasets i.e. CASIA v2.0, DVMM, IFS-TC, CASIA v1.0, Columbia, and DSO-1. Moreover, the SVM classifier is trained to classify the images as tampered or authentic. The effectiveness of the proposed scheme is evaluated based on various performance parameters such as accuracy, sensitivity, specificity, and informedness. The proposed results show improved accuracy i.e. 99.69%, 99.76%, 97.80%, 98.61%, 96.90%, and 92.50% on CASIA v1.0, CASIA v2.0, DVMM, Columbia, IFS-TC, and DSO-1, respectively, in comparison to other existing approaches.

Novel Data-Access Scheme and Efficient Parallel Architecture for Multi-level Lifting 2-D DWT

Data-access scheme affects the complexity of parallel architectures performing computation of multi-level two-dimensional (2-D) discrete wavelet transform (DWT). In this paper, we made a study on the data-access schemes considered in the existing parallel 2-D DWT architectures. Based on this study, a novel data-access scheme is proposed to avoid data multiplexing, which is common in the existing parallel architectures. Further, a block formulation is presented for vector computation of multi-level lifting 2-D DWT. A generic design of processing unit for computing one-level 2-D DWT is derived using the proposed block formulation. The proposed generic design resizes by a single parameter, i.e., the input-vector size. A regular and modular parallel architecture is derived using the generic processing unit design. The proposed parallel architecture easily scalable for higher block sizes as well as higher DWT levels without sacrificing its circuit regularity and modularity. This is an important feature of the proposed architecture. Comparison result shows that the proposed architecture for three-level DWT and block size 64 offers a saving in 24% area–delay–product (ADP) and 10% power consumption, and higher saving for higher block sizes than the best available similar structure without any overhead memory unlike the existing structure.

Hardware design of multiclass SVM classification for epilepsy and epileptic seizure detection

An automatic detection system for distinguishing healthy, ictal, and inter-ictal EEG signals plays an important role in medical practice. This paper presents a very large scale integration (VLSI) architecture of three-class classification for epilepsy and seizure detection. In order to find out the most efficient three-class classification scheme for hardware implementation, several multiclass non-linear support vector machine (NLSVM) classifiers are compared and validated using software implementation. Finally, the one-against-one (OAO) multiclass NLSVM is selected due to its highest accuracy. The designed system consists of a discrete wavelet transform (DWT)-based feature extraction module, a modified sequential minimal optimization (MSMO) training module, and an OAO multiclass classification module. A lifting structure of Daubechies order 4 wavelet is introduced in three-level DWT to save circuit area and speed up the computational time. The MSMO is used for on-chip training. The circuit of the largest absolute value decision is designed to avoid the unclassifiable problem in the OAO multiclass classification. The designed system is implemented on a field-programmable gate array (FPGA) platform and evaluated using the publicly available epilepsy dataset. The experimental results demonstrate that the designed system achieves high accuracy with low-dimensional feature vectors.

A robust embedding and blind extraction of image watermarking based on discrete wavelet transform

In this study, a new algorithm that can be used for copyright protection is proposed for the embedding and detection of a watermark in the domain of discrete wavelet transform (DWT). The elements of the logo watermark are embedded directly to the three-level DWT decomposed subbands. In addition to that, the scheme is considered as a completely blind scheme for both host image and watermark. Two kinds of security attacks have also been considered which confirms the security of the scheme. The experimental results and computer simulation of the proposed scheme have shown that the proposed approach has the desired properties such as invisibility, blind detection, and robustness against various geometrical and non-geometrical attacks.

Hardware efficient multiplier‐less multi‐level 2D DWT architecture without off‐chip RAM

This study presents a multi-level 2D discrete wavelet transform (DWT) architecture without off-chip RAM. Existing architectures use one off-chip RAM to store the image data, which increases the complexity of the system. For one-chip design, line-based architecture based on modified lifting scheme is proposed. By replacing the multipliers with canonic sign digit multipliers, a critical path of one full-adder delay is achieved. As per theoretical estimate, for three-level 2D DWT with an image of N × N size, the proposed architecture requires 123 adders, 66 subtracters, 167 registers, temporal memory of 7.5N words and input RAM of 3N bytes. The estimated hardware requirement shows that for the image size of 512 × 512 and three-level DWT, the proposed architecture involves at least 14.1% less transistor-delay-product than existing architectures.

Complexity Analysis of Electroencephalogram Dynamics in Patients with Parkinson's Disease.

In this study, a new combination scheme has been proposed for detecting Parkinson's disease (PD) from electroencephalogram (EEG) signal recorded from normal subjects and PD patients. The scheme is based on discrete wavelet transform (DWT), sample entropy (SampEn), and the three-way decision model in analysis of EEG signal. The EEG signal is noisy and nonstationary, and, as a consequence, it becomes difficult to distinguish it visually. However, the scheme is a well-established methodology in analysis of EEG signal in three stages. In the first stage, the DWT was applied to acquire the split frequency information; here, we use three-level DWT to decompose EEG signal into approximation and detail coefficients; in this stage, we aim to remove the useless and noise information and acquire the effective information. In the second stage, as the SampEn has advantage in analyzing the EEG signal, we use the approximation coefficient to compute the SampEn values. Finally, we detect the PD patients using three-way decision based on optimal center constructive covering algorithm (O_CCA) with the accuracy about 92.86%. Without DWT as preprocessing step, the detection rate reduces to 88.10%. Overall, the combination scheme we proposed is suitable and efficient in analyzing the EEG signal with higher accuracy.