A secured image steganography method based on ballot transform and genetic algorithm

Multimedia Tools and Applications - Rapid advances in digital technology have facilitated us to transfer a huge amount of electronic files over the internet. But in the presence of malicious...     

Enhanced high capacity image steganography using discrete wavelet transform and the Laplacian pyramid

Multimedia Tools and Applications - This paper introduces a high capacity image hiding scheme with enhanced stego image quality. This new hiding scheme utilizes a multiscale Laplacian pyramid of...     

An image steganography method based on integer wavelet transform

This paper presents a novel approach for Image steganography based on Integer Wavelet Transform. In this method, the cover image is mapped to a specific frequency domain. Then in the obtained frequency domain, edge coefficients are classified based on their MSBs. The suggested method prevents changes in MSB in a way that receiver can extract the information without any mistakes. Considering the preformed classification, secret bits will be embedded in the frequency coefficients and then with the use of inverse transformation, stego image will be obtained. The most important features that our work obtained are good adaptation with human vision system and retrieval of data without error. Simulation results show that our proposed method has a good adaptation with human vision system (HVS) and outperforms in terms of PSPNR factors over recently published works.     

Application of bi-orthogonal wavelet transform and genetic algorithm in image steganography

Multimedia Tools and Applications - The Internet is used for exchanging information. Sometimes it is required to transmit confidential data over the internet. Here the authors use image...     

Optimal wavelet transform using Oppositional Grey Wolf Optimization for video steganography

Data hiding in video is a method used to hide secret information within the video which is useful for secure multimedia data communication. The main goal of any video steganographic system is to reduce distortion of video and to secure the embedded data. A novel approach of hiding data using Oppositional Grey Wolf Optimization (OGWO) is proposed to minimize distortion and to enhance security so as to get superior video quality. In this work, scene changes are used to identify the key frames to hide the secret data. The scene changes are detected using Discrete Cosine Transform (DCT). Once the key frames are detected, OGWO is used at this stage to select the optimal region to embed the secret data. Lastly, the optimal region for entrenching is construed to embed the secret data using Discrete Wavelet Transform (DWT). Then, the payload and video are normalized using Inverse DWT to boost the video quality. The performance of the proposed system is measured using Peak Signal to Noise Ratio (PSNR), Embedding Capacity and Normalized Correlation (NC). The comparison results show that the proposed method delivers more security and minimizes distortions for improved video quality.

     

Secure image steganography using framelet transform and bidiagonal SVD

Multimedia Tools and Applications - Steganography and steganalysis are the prominent research fields in information hiding paradigm. This work presents a novel framelet transform based image...     

Medical image analysis using wavelet transform and deep belief networks

This paper introduces a three-step framework for classifying multiclass radiography images. The first step utilizes a de-noising technique based on wavelet transform (WT) and the statistical Kolmogorov Smirnov (KS) test to remove noise and insignificant features of the images. An unsupervised deep belief network (DBN) is designed for learning the unlabelled features in the second step. Although small-scale DBNs have demonstrated significant potential, the computational cost of training the restricted Boltzmann machine is a major issue when scaling to large networks. Moreover, noise in radiography images can cause a significant corruption of information that hinders the performance of DBNs. The combination of WT and KS test in the first step helps improve performance of DBNs. Discriminative feature subsets obtained in the first two steps serve as inputs into classifiers in the third step for evaluations. Five frequently used classifiers including naive Bayes, radial basis function network, random forest, sequential minimal optimization, and support vector machine and four different case studies are implemented for experiments using the Image Retrieval in Medical Application data set. The experimental results show that the three-step framework has significantly reduced computational cost and yielded a great performance for multiclass radiography image classification. Along with effective applications in image processing in other fields published in the literature, deep learning network in this paper has again demonstrated its robustness in handling a complex set of medical images. This implies that the proposed approach can be implemented in real practice for analysing noisy radiography images, which have many useful medical applications such as diagnosis of diseases related to lung, breast, musculoskeletal or pediatric studies.     

Hyperspectral image classification using graph-based wavelet transform

ABSTRACT

Graph-based methods are developed to efficiently extract data information. In particular, these methods are adopted for high-dimensional data classification by exploiting information residing on weighted graphs. In this paper, we propose a new hyperspectral texture classifier based on graph-based wavelet transform. This recent graph transform allows extracting textural features from a constructed weighted graph using sparse representative pixels of hyperspectral image. Different measurements of spectral similarity between representative pixels are tested to decorrelate close pixels and improve the classification precision. To achieve the hyperspectral texture classification, Support Vector Machine is applied on spectral graph wavelet coefficients. Experimental results obtained by applying the proposed approach on Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Reflective Optics System Imaging Spectrometer (ROSIS) datasets provide good accuracy which could exceed 98.7%. Compared to other famous classification methods as conventional deep learning-based methods, the proposed method achieves better classification performance. Results have shown the effectiveness of the method in terms of robustness and accuracy.     

A security enhanced robust audio steganography algorithm for image hiding using sample comparison in discrete wavelet transform domain and RSA encryption

Multimedia Tools and Applications - Steganography is the technique of hiding any secret information like text, image or video behind a cover file. Audio steganography is one of the widespread data...     

A novel joint secret image sharing and robust steganography method using wavelet

In this paper a data hiding method is proposed based on the combination of a secret sharing technique and a novel steganography method using integer wavelet transform. In this method in encoding phase, first a secret image is shared into n shares, using a secret sharing technique. Then, the shares and Fletcher-16 checksum of shares are hidden into n cover images using proposed wavelet based steganography method. In decoding phase, t out of n stego images are required to recover the secret image. In this phase, first t shares and their checksums are extracted from t stego images. Then, by using the Lagrange interpolation the secret image is revealed from the t shares. The proposed method is stable against serious attacks, including RS and supervisory training steganalysis methods, it has the lowest detection rate under global feature extraction classifier examination compared to the state-of-the-art techniques. Experimental results on a set of benchmarks showed that this method outperforms conventional methods in offering a high secure and robust mechanism for joining secret image sharing and steganography.     

Performance enhanced image steganography systems using transforms and optimization techniques

Image steganography is the art of hiding highly sensitive information onto the cover image. An ideal approach to image steganography must satisfy two factors: high quality of stego image and high embedding capacity. Conventionally, transform based techniques are widely preferred for these applications. The commonly used transforms for steganography applications are Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) etc. In this work, frequency domain transforms such as Fresnelet Transform (FT) and Contourlet Transform (CT) are used for the data hiding process. The secret data is normally hidden in the coefficients of these transforms. However, data hiding in transform coefficients yield less accurate results since the coefficients used for data hiding are selected randomly. Hence, in this work, optimization techniques such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used for improving the performance of the steganography system. GA and PSO are used to find the best coefficients in order to hide the Quick Response (QR) coded secret data. This approach yields an average PSNR of 52.56 dB and an embedding capacity of 902,136 bits. These experimental results validate the practical feasibility of the proposed methodology for security applications.     

Multi-resolution image representation by using polychromatic wavelet transform

This paper proposes a new method for performing multi-resolution image representation by using polychromatic wavelet transform. The proposed method has an advantage in that multi-resolution outputs can be simultaneously obtained. Preliminary experimental results are presented.     

Context-based embedded image compression using binary wavelet transform

Binary wavelet transform (BWT) has several distinct advantages over the real wavelet transform (RWT), such as the conservation of alphabet size of wavelet coefficients, no quantization introduced during the transform and the simple Boolean operations involved. Thus, less coding passes are engaged and no sign bits are required in the compression of transformed coefficients. However, the use of BWT for the embedded grayscale image compression is not well established. This paper proposes a novel Context-based Binary Wavelet Transform Coding approach (CBWTC) that combines the BWT with a high-order context-based arithmetic coding scheme to embedded compression of grayscale images. In our CBWTC algorithm, BWT is applied to decorrelate the linear correlations among image coefficients without expansion of the alphabet size of symbols. In order to match up with the CBWTC algorithm, we employ the gray code representation (GCR) to remove the statistical dependencies among bi-level bitplane images and develop a combined arithmetic coding scheme. In the proposed combined arithmetic coding scheme, three highpass BWT coefficients at the same location are combined to form an octave symbol and then encoded with a ternary arithmetic coder. In this way, the compression performance of our CBWTC algorithm is improved in that it not only alleviate the degradation of predictability caused by the BWT, but also eliminate the correlation of BWT coefficients in the same level subbands. The conditional context of the CBWTC is properly modeled by exploiting the characteristics of the BWT as well as taking advantages of non-causal adaptive context modeling. Experimental results show that the average coding performance of the CBWTC is superior to that of the state-of-the-art grayscale image coders, and always outperforms the JBIG2 algorithm and other BWT-based binary coding technique for a set of test images with different characteristics and resolutions.     

一种基于提升小波变换和矩阵编码的音频隐写算法

以提高隐秘信息嵌入量与隐蔽性为主要目的,利用人耳听觉系统（HAS）的掩蔽效应,提出一种结合提升小波变换和矩阵编码的嵌入隐秘信息的音频隐写算法。该算法利用MPEG I心理声学模型1来控制嵌入帧,选用宿主音频提升小波变换的中低频系数,利用能够大幅提高嵌入效率、减小修改比例的矩阵编码来实现隐秘信息的嵌入。经实验仿真证明,该算法不仅具有很好的隐蔽性和嵌入容量,还兼顾了鲁棒性,对于加噪、滤波、重采样、MP3压缩、同步攻击等常见操作具有较强的抵抗力。同时,该算法能够实现盲检测。     

结合小波变换和图像主元分析的人脸识别

提出了一种基于小波变换和图像主元分析（IMPCA）相结合的人脸识别方法。小波变换具有保留主要信息,去除噪声的作用,对人脸图像进行小波变换,对变换后的近似图像采用IMPCA方法进行识别。IMPCA是一种快速有效的直接通过图像抽取特征的方法,从图像重构的角度分析了实现IMPCA的两种模式,两种模式分别增强了图像的行特征和列特征,将它们的识别结果进行决策融合可以获得更好的识别效果。基于ORL人脸数据库的实验表明,所提出的方法在识别率上优于单独的IMPCA方法。     

基于四元数小波变换的清晰度评价

针对当前各种图像清晰度评价方法在清晰度判别过程中单调性和区分度不够以及适用范围较小的问题,提出了一种基于四元数小波变换（QWT）幅值与相位的图像清晰度评价方法。该算法通过四元数小波变换将图像从空间域变换到频率域,对得到的四元数小波变换系数进一步计算之后获得低频子带与高频子带的幅值与相位信息,求得低频子带幅值各方向的梯度之后与对应方向的相位相乘求和,最终得到两个清晰度指标值。采用该算法与多种现有算法对不同内容的图像、不同程度模糊的图像以及含有不同程度噪声的图像进行清晰度评价实验：相对于现有算法,所提算法在对上述多种图像的清晰度评价中都保持着很好的单调性与区分度。实验结果表明,所提算法不但克服了现有算法在单调性与区分度上的不足,而且所提清晰度评价指标可以应用在图像处理中。     

小波变换和变分PDE相结合的图像去噪算法

为了更好地恢复图像,利用小波变换的思想,提出了一种变分和小波变换相结合的图像去噪算法。该算法的思想是先构造一个用带有韦伯心理学的范数估计图像正则性的变分泛函,然后在小波域中最小化变分泛函得到还原图像。与传统的直接求泛函最小化的问题有区别,该算法是用变分的思想再结合小波变换进行图像去噪。小波变换后的高频分量具有丰富的细节边缘信息,因而能够重构出高质量的图像,而且小波的引入使得新算法具有运行时间短、速度快的特点。理论分析和实验仿真表明,该算法能达到比单一方法更好的恢复效果。     

Vector quantizer of medical image using wavelet transform and enhanced SOM algorithm

Vector quantizer takes care of special image features like edges, and it belongs to the class of quantizers known as the second-generation coders. This paper proposes a novel vector quantization method using the wavelet transform and the enhanced SOM algorithm for the medical image compression. We propose the enhanced self-organizing algorithm to resolve the defects of the conventional SOM algorithm. The enhanced SOM, at first, reflects the error between the winner node and the input vector to the weight adaptation by using the frequency of the selection of the winner node. Secondly, it adjusts the weight in proportion to the present weight change and the previous one as well. To reduce the blocking effect and the computation requirement, we construct training image vectors involving image features by using the wavelet transform and apply the enhanced SOM algorithm to them for generating a well-defined codebook. Our experimental results have shown that the proposed method energizes the compression ratio and the decompression quality.