A Recursive High Payload Reversible Data Hiding Using Integer Wavelet and Arnold Transform

Reversible data hiding is an information hiding technique that requires the retrieval of the error free cover image after the extraction of the secret image.We suggested a technique in this research that uses a recursive embedding method to increase capacity substantially using the Integer wavelet transform and the Arnold transform.The notion of Integer wavelet transforms is to ensure that all coefficients of the cover images are used during embedding with an increase in payload.By scrambling the cover image,Arnold transform adds security to the information that gets embedded and also allows embedding more information in each iteration.The hybrid combination of Integer wavelet transform and Arnold transform results to build a more efficient and secure system.The proposed method employs a set of keys to ensure that information cannot be decoded by an attacker.The experimental results show that it aids in the development of a more secure storage system and withstand few tampering attacks The suggested technique is tested on many image formats,including medical images.Various performance metrics proves that the retrieved cover image and hidden image are both intact.This System is proven to withstand rotation attack as well.

A secure image steganography algorithm based on least significant bit and integer wavelet transform

The rapid development of data communication in modern era demands secure exchange of information. Steganography is an established method for hiding secret data from an unauthorized access into a cover object in such a way that it is invisible to human eyes. The cover object can be image, text, audio,or video. This paper proposes a secure steganography algorithm that hides a bitstream of the secret text into the least significant bits（LSBs） of the approximation coefficients of the integer wavelet transform（IWT） of grayscale images as well as each component of color images to form stego-images. The embedding and extracting phases of the proposed steganography algorithms are performed using the MATLAB software. Invisibility, payload capacity, and security in terms of peak signal to noise ratio（PSNR） and robustness are the key challenges to steganography. The statistical distortion between the cover images and the stego-images is measured by using the mean square error（MSE） and the PSNR, while the degree of closeness between them is evaluated using the normalized cross correlation（NCC）. The experimental results show that, the proposed algorithms can hide the secret text with a large payload capacity with a high level of security and a higher invisibility. Furthermore, the proposed technique is computationally efficient and better results for both PSNR and NCC are achieved compared with the previous algorithms.

Seismic data compression based on integer wavelet transform

Due to the particularity of the seismic data, they must be treated by lossless compression algorithm in some cases. In the paper, based on the integer wavelet transform, the lossless compression algorithm is studied. Comparing with the traditional algorithm, it can better improve the compression rate. CDF (2, n) biorthogonal wavelet family can lead to better compression ratio than other CDF family, SWE and CRF, which is owe to its capability in can- celing data redundancies and focusing data characteristics. CDF (2, n) family is suitable as the wavelet function of the lossless compression seismic data.

An Approach to Integer Wavelet Transform for Medical Image Compression in PACS

We study an approach to integer wavelet transform for lossless compression of medical image in medical picture archiving and communication system (PACS). By lifting scheme a reversible integer wavelet transform is generated, which has the similar features with the corresponding biorthogonal wavelet transform. Experimental results of the method based on integer wavelet transform are given to show better performance and great applicable potentiality in medical image compression.

A COMPRESSION ALGORITHM FOR ECG BASED ON INTEGER LIFTING SCHEME WAVELET TRANSFORM

In view of the shortcomes of conventional ElectroCardioGram (ECG) compression algo- rithms,such as high complexity of operation and distortion of reconstructed signal,a new ECG compression encoding algorithm based on Set Partitioning In Hierarchical Trees (SPIHT) is brought out after studying the integer lifting scheme wavelet transform in detail.The proposed algorithm modifies zero-tree structure of SPIHT,establishes single dimensional wavelet coefficient tree of ECG signals and enhances the efficiency of SPIHT-encoding by distributing bits rationally,improving zero-tree set and ameliorating classifying method.For this improved algorithm,floating-point com- putation and storage are left out of consideration and it is easy to be implemented by hardware and software.Experimental results prove that the new algorithm has admirable features of low complexity, high speed and good performance in signal reconstruction.High compression ratio is obtained with high signal fidelity as well.

Integer Wavelet-Based Image Interpolation in Lifting Structure for Image Resolution Enhancement

A floating-point wavelet-based and an integer wavelet-based image interpolations in lifting structures and polynomial curve fitting for image resolution enhancement are proposed in this paper. The proposed prediction methods estimate high-frequency wavelet coefficients of the original image based on the available low-frequency wavelet coefficients, so that the original image can be reconstructed by using the proposed prediction method. To further improve the reconstruction performance, we use polynomial curve fitting to build relationships between actual high-frequency wavelet coefficients and estimated high-frequency wavelet coefficients. Results of the proposed prediction algorithm for different wavelet transforms are compared to show the proposed prediction algorithm outperforms other methods.

Meaningful image encryption algorithm based on compressive sensing and integer wavelet transform

A new meaningful image encryption algorithm based on compressive sensing(CS)and integer wavelet transformation(IWT)is proposed in this study.First of all,the initial values of chaotic system are encrypted by RSA algorithm,and then they are open as public keys.To make the chaotic sequence more random,a mathematical model is constructed to improve the random performance.Then,the plain image is compressed and encrypted to obtain the secret image.Secondly,the secret image is inserted with numbers zero to extend its size same to the plain image.After applying IWT to the carrier image and discrete wavelet transformation(DWT)to the inserted image,the secret image is embedded into the carrier image.Finally,a meaningful carrier image embedded with secret plain image can be obtained by inverse IWT.Here,the measurement matrix is built by both chaotic system and Hadamard matrix,which not only retains the characteristics of Hadamard matrix,but also has the property of control and synchronization of chaotic system.Especially,information entropy of the plain image is employed to produce the initial conditions of chaotic system.As a result,the proposed algorithm can resist known-plaintext attack(KPA)and chosen-plaintext attack(CPA).By the help of asymmetric cipher algorithm RSA,no extra transmission is needed in the communication.Experimental simulations show that the normalized correlation(NC)values between the host image and the cipher image are high.That is to say,the proposed encryption algorithm is imperceptible and has good hiding effect.

适合水声隐蔽通信的图像隐写算法

为了实现水声信道场景中的快速、安全信息隐藏,提出一种基于杜鹃搜索(CS)与整数小波变换的水声隐蔽通信下图像隐写算法.首先对待嵌入的水下图像进行交织打包处理,生成秘密信息码流,提高传输差错检测和恢复能力;然后将载体图像划分为不重叠的块,对每个子块应用整数小波变换进行处理,既可以节省CS搜索空间,又能提高算法的寻优性能;再对高频子带系数的嵌入位置矩阵进行混沌置乱,使用CS选择最优密钥获得秘密信息的最优匹配位置,产生最佳质量的隐秘图像;最后对提取出的秘密信息采用传输差错检测与恢复措施,提高水声信道传输后水下图像的重建质量.在标准测试图像数据集SIPI上进行实验结果表明,当信息嵌入容量为0.5 bit/像素时,所提算法生成隐秘图像的平均峰值信噪比(PSNR)达到52.80 dB,平均数据结构相似性指数度量为0.9999,优于现有算法,满足水下通信安全性、实时性的要求;当丢包率高达20%时,重建图像的PSNR超过35.9 dB,在0.8%的混合误码率下,重建图像的PSNR超过36 dB,具有应对恶劣水下噪声环境下传输差错的检测与恢复能力.

基于物联网的无线通信数据隐藏方案

为了在数据传输过程中,避免机密数据出现泄露的风险,设计了一种无线通信数据隐藏方案。数据隐藏方案在基于物联网的安全通信中发挥着至关重要的作用,数据隐藏使用图像作为输入工具,因为图像易于访问,数据隐藏的目的是确保消息的机密性。提出的方案对物联网中传输的通信数据图像进行整数小波变换,将其解析为四个连续的频带,并评估了用于数据隐藏的HH频带的特征分解值。将提出的方案与传统的编码转换数据隐藏方法在Matlab中进行了仿真,结果表明,与传统方案相比,提出方案具有较高的峰值信噪比,证明了该方案在基于物联网通信中具有较高的应用价值,可保证传输数据的机密性。

Second Generation Wavelet Applied to Lossless Compression Coding of Image

In this paper, the second generation wavelet transform is applied to image lossless coding, according to its characteristic of reversible integer wavelet transform. The second generation wavelet transform can provide higher compression ratio than Huffman coding while it reconstructs image without loss compared with the first generation wavelet transform. The experimental results show that the se cond generation wavelet transform can obtain excellent performance in medical image compression coding.

Image Watermarking Method Using Integer-to-Integer Wavelet Transforms

Digital watermarking is an efficient method for copyright protection for text, image, audio, and video data. This paper presents a new image watermarking method based on integer-to-integer wavelet transforms. The watermark is embedded in the significant wavelet coefficients by a simple exclusive OR operation. The method avoids complicated computations and high computer memory requirements that are the main drawbacks of common frequency domain based watermarking algorithms. Simulation results show that the embedded watermark is perceptually invisible and robust to various operations, such as low quality joint picture expert group (JPEG) compression, random and Gaussian noises, and smoothing (mean filtering).

Medical image lossless compression based on combining an integer wavelet transform with DPCM

To improve the classical lossless compression of low efficiency,a method of image lossless compression with high efficiency is presented.Its theory and the algorithm implementation are introduced.The basic approach of medical image lossless compression is then briefly described.After analyzing and implementing differential plus code modulation(DPCM)in lossless compression,a new method of combining an integer wavelet transform with DPCM to compress medical images is discussed.The analysis and simulation results show that this new method is simpler and useful.Moreover,it has high compression ratio in medical image lossless compression.

基于小波变换及亮度分量的多功能数字水印算法

研究改进彩色图像多功能水印构造算法。针对传统多功能水印算法构造出的健壮水印与脆弱水印容易相互影响,且多功能水印大多为无意义水印的现状,提出了一种新的有意义的彩色图像多功能水印算法。将彩色图像的Y分量图像分块,选取与分块个数具有相同像素点数的二值图像作为水印信息,将每个像素点的值重复嵌入到彩色图像Y分量对应各分块经小波变换后的四个子带中。通过正确提取LL子带中的水印信息实现版权证明功能,通过比较原二值水印图像与从四个子带中分别提取出的水印图像,实现图像内容完整性证明。实验结果证明,该多功能水印对噪声、JPEG压缩、滤波具有良好的稳健性;具有较高的图像篡改检测率,准确的篡改定位能力,且水印提取为盲提取。

Application of Zero-Watermarking for Medical Image in Intelligent Sensor Network Security

The field of healthcare is considered to be the most promising application of intelligent sensor networks.However,the security and privacy protection ofmedical images collected by intelligent sensor networks is a hot problem that has attracted more and more attention.Fortunately,digital watermarking provides an effective method to solve this problem.In order to improve the robustness of the medical image watermarking scheme,in this paper,we propose a novel zero-watermarking algorithm with the integer wavelet transform(IWT),Schur decomposition and image block energy.Specifically,we first use IWT to extract low-frequency information and divide them into non-overlapping blocks,then we decompose the sub-blocks by Schur decomposition.After that,the feature matrix is constructed according to the relationship between the image block energy and the whole image energy.At the same time,we encrypt watermarking with the logistic chaotic position scrambling.Finally,the zero-watermarking is obtained by XOR operation with the encrypted watermarking.Three indexes of peak signal-to-noise ratio,normalization coefficient(NC)and the bit error rate(BER)are used to evaluate the robustness of the algorithm.According to the experimental results,most of the NC values are around 0.9 under various attacks,while the BER values are very close to 0.These experimental results show that the proposed algorithm is more robust than the existing zero-watermarking methods,which indicates it is more suitable for medical image privacy and security protection.

Brain Tumor Classification Using Image Fusion and EFPA-SVM Classifier

An accurate and early diagnosis of brain tumors based on medical ima-ging modalities is of great interest because brain tumors are a harmful threat to a person’s health worldwide.Several medical imaging techniques have been used to analyze brain tumors,including computed tomography(CT)and magnetic reso-nance imaging(MRI).CT provides information about dense tissues,whereas MRI gives information about soft tissues.However,the fusion of CT and MRI images has little effect on enhancing the accuracy of the diagnosis of brain tumors.Therefore,machine learning methods have been adopted to diagnose brain tumors in recent years.This paper intends to develop a novel scheme to detect and classify brain tumors based on fused CT and MRI images.The pro-posed approach starts with preprocessing the images to reduce the noise.Then,fusion rules are applied to get the fused image,and a segmentation algorithm is employed to isolate the tumor region from the background to isolate the tumor region.Finally,a machine learning classifier classified the brain images into benign and malignant tumors.Computing statistical measures evaluate the classi-fication potential of the proposed scheme.Experimental outcomes are provided,and the Enhanced Flower Pollination Algorithm(EFPA)system shows that it out-performs other brain tumor classification methods considered for comparison.

基于一维压缩感知和混沌的医学图像加密研究

医学图像由于涉及隐私等重要问题,其安全性问题逐渐凸显,同时其具有数据量大、同色像素连续性高等特点。为实现医学图像安全、快速地在网络中传输,提出一种基于压缩感知和同步混沌流密码的医学图像加密方案。首先迭代三维同步混沌流密码系统构造出测量矩阵和得到用于加密的伪随机序列。然后,利用测量矩阵对整数小波变换后的稀疏医学图像进行测量,并使用量化函数将测量后的图像量化至[0,255]范围内的整数。最后,利用伪随机序列加密量化后的图像。相应的解密算法也同时给出。从以CT图像为例的实验仿真结果可知,该方案具有良好的压缩和加密性能。同时,相关安全性能分析结果表明,所提方案对统计攻击和剪切攻击具有良好的安全性和鲁棒性。

基于整数小波变换和SVD的视频水印算法

提出了一种以二值图像为水印的混合整数小波变换和奇异值分解的视频水印盲提取算法。对水印图像进行混沌加密和Arnold置乱处理,选择计算复杂度低的直方图算法将视频分割为若干场景;借助密钥随机选取某些场景的亮度分量进行l级整数小波变换,再对低频子带进行分块的奇异值分解;采用量化的方法,将预处理后的水印图像嵌入奇异值分解后的最大奇异值中。在嵌入了水印的视频场景中提取所有的水印版本之后,利用对提取的所有水印信号版本进行统计求和的方法得到最终提取的水印图像。实验表明,提出的算法具有较好的透明性,对常见的处理具有较好的鲁棒性。

基于整数小波变换的多光谱图像无损压缩

多光谱图像一般都采用预测方法去除空间冗余和谱内冗余实现无损压缩。通过用提升方法构造整数小波变换 ,将变换方法用于去除空间冗余 ;通过分类方法构造谱间预测器 ,用预测方法去除谱间冗余 ,两者相结合 ,实现无损压缩。由于变换方法的去相关性能良好 ,使该方法压缩效果大大改善。

利用Morlet连续小波变换实现非整次谐波的检测

针对常规快速傅立叶变换无法检测非整次谐波的问题,文章提出了利用小波变换实现检测非整次谐波的方法。小波函数是具有时域和频域良好局部化特性的函数,理论上可以用于非整次谐波的检测。但是小波变换存在着由于频谱泄露而带来的混频问题,给谐波的检测带来误差。为解决此问题,可选择分频严格的小波函数,或者选择合适的分析方法。该文选用的分析方法是连续小波变换,选用的基小波是具有良好频域特性的Morlet小波。通过matlab仿真,在1个周期的采样数据中,能够较为准确地把不同频率的整次和非整次谐波分离出来。这表明了小波变换对于非整次谐波的检测和分析是可行的,从而为谐波的精确检测提供了有效的手段。

基于提升框架的整数小波变换

提升框架可以从一个新的角度来构造小波滤波器,它的核心是确定预测算子和更新算子。该文 引进了多项式局部拟合方法来求取预测算子和更新算子,对每步提升下取整,就实现了整数小波变换.仿真 结果表明,该方法应用于二维数字图像,在一定失真的前提下,具有较高的压缩比,而且小波系数与尺度系 数都是整数,不需要量化步骤.因此,它对于整数数据压缩具有重要的意义.