利用差值扩展进行可逆数据隐藏的新算法

基于整数哈尔(Haar)小波变换,提出一种新的利用横向和纵向差值图像进行扩展嵌入的数据隐藏算法。传统的差值嵌入技术最大的缺点是在第二重嵌入开始之前图像的质量已经遭到破坏,原因是前一重嵌入用到了很大的差值。针对这个问题,该方法动态地把负载分配到两个正交方向上的图像进行嵌入。尽量使这两个方向上用于嵌入的差值属于同一个幅值范围。与其他算法相比,本算法在同等嵌入率下可取得更好的图像质量。     

用于矢量数字地图的可逆数据隐藏算法

提出一种基于差值直方图的可逆数据隐藏算法,用于矢量地图的认证和隐秘通信。通过修改相邻顶点坐标的差值来实现数据隐藏。为减少由扩展嵌入所引起的图形失真,借助差值直方图来选取可嵌入差值,优先选择绝对值小的差值。与现有算法相比,本算法具有较高的嵌入率、较好的图形质量以及精确控制嵌入容量的能力。     

结合层次结构和直方图平移的无损数据隐藏

针对现有基于差值直方图方法利用原始图像结构关系上的不足,提出了一种基于层次结构和差值直方图平移的无损数据隐藏方法RDH-HSDHS。RDH-HSDHS利用原始图像数据块中像素的差值形成直方图,充分利用图像中相邻像素间的相关性嵌入数据,为了进一步利用数据块中的参考像素进行数据嵌入,将参考像素组成新的图像进行下一层水印嵌入,直到当前层的嵌入容量小于解码所需的附加信息的长度或隐秘图像质量小于给定阈值。实验仿真结果表明,RDH-HSDHS能较好利用原始图像的全局和局部特性,在嵌入容量和隐秘图像质量之间达到较好的折中,在隐秘图像质量超过30 dB的同时,嵌入容量大于0.75 bit/pixel。另外,与相似方法的性能比较证明了提出方法的优势。该方法能有效应用于高质量需求的图像载体中进行信息隐藏。     

Secure and imperceptible digital image steganographic algorithm based on diamond encoding in DWT domain

This paper presents a new efficient embedding algorithm in the wavelet domain of digital images based on the diamond encoding (DE) scheme. Current discrete wavelet transform (DWT) steganography adds an unacceptable distortion to the images and is considered as an ineffective in terms of security. Applying the DE scheme to the current DWT steganographic methods solves the problems of these methods, and reduces the distortion added to the images, and thus improves the embedding efficiency. The proposed algorithm first converts the secret image into a sequence of base-5 digits. After that, the cover image is transformed into the DWT domain and segmented into 2?×?1 coefficient pairs. The DE scheme is used then to change at most one coefficient of each coefficient pair to embed the base-5 digits. Experimental results depict that the proposed algorithm is more efficient in embedding compared to other methods in terms of embedding payload and image quality. Moreover, the proposed algorithm is attacked by well-known steganalysis software. Results are showing that the proposed algorithm is secure against the powerful universal steganalyzer “ensemble classifier” and the histogram attack. The results also reveal that the proposed algorithm is robust against different image processing attacks such as compression, added noise, and cropping attacks.     

边缘和纹理优先的可逆数据隐藏算法

为了提高加水印图像的感知质量,对基于图像像素预测误差的嵌入算法进行了研究。优先选择了图像的边缘和纹理进行差值扩展嵌入,并结合压缩性非常高的溢出位置图,实现了高视觉质量、大嵌入容量的可逆数据隐藏算法。     

High capacity data hiding schemes for medical images based on difference expansion

Since the difference expansion (DE) technique was proposed, many researchers tried to, improve its performance in terms of hiding capacity and visual quality. In this paper, a new scheme, based on DE is proposed in order to increase the hiding capacity for medical images. One of the characteristics of medical images, among the other types of images, is the large smooth regions. Taking advantage of this characteristic, our scheme divides the image into two regions; smooth region and non-smooth region. For the smooth region, a high embedding capacity scheme is applied, while the original DE method is applied to the non-smooth region. Sixteen DICOM images of different modalities were used for testing the proposed schemes. The results showed that the proposed scheme has higher hiding capacity compared to the original schemes.     

High-capacity PVO-based reversible data hiding scheme using changeable step size

Reversible data hiding (RDH) algorithm based on pixel-value-ordering (PVO) has received widespread attention because of its excellent performance. PVO algorithm divides the host image into non-overlapped equal-sized blocks, then achieves data embedding by modifying the maximum and minimum values of each block. Every pixel block can be a host of watermark data, so the smaller number of pixel blocks limit the embedding capacity (EC). In our work, a novel PVO with changeable step size (CPVO) is presented which can choose suitable step size based on the number of watermark data bits, even that allow one block to overlap other ones. Take the block size 2 × 2 as an example, we can set step size 2 × 1 or another one in CPVO. Consequently, with a block selection skill based on the noise level (NL) of a pixel block, CPVO can embed more hidden data bits into a host image. Compared to the original PVO-based schemes, experimental results show that our proposed scheme increases the EC by 2 ～ 3 times, and the marked image quality keeps much higher, even outperforms some other state-of-the-art works in some test images.

     

自适应变异差分算法与Powell算法相结合的医学图像配准

图像配准是医学图像处理中的关键技术。文中提出一种自适应差分算法(Difference Algorithm,DE)和Powell算法相结合的多分辨率医学图像配准方法,其不仅可以克服Powell算法依赖初始点的缺点,还可以降低陷入局部极值的几率。首先,对源图像进行多分辨处理,获得包括源图像在内的三层图像;然后,在低分辨率图像上使用自适应DE算法进行全局变换参数的搜索,获得的变换参数作为Powell算法的初始点;最后,在高分辨率图像及源图像上使用Powell算法进行配准。与传统实验相比,该方法具有更高的精确度,能够有效避免局部收敛问题。     

应用五株采样提升算法的抗盲检测图像隐写算法

在分析图像盲检测算法原理的基础上,提出了一种在形态小波高频系数上进行消息嵌入的抗盲检测隐写算法.该算法利用五株采样提升实现图像的小波变换,在大于一定门限的小波高频系数中嵌入消息,并通过建立的嵌入信息表来修正嵌入规则以保持小波系数直方图近似不变,在门限处引入直方图调整策略以减小系数直方图在门限处的变化.由于通用盲检测算法大多基于概率密度函数的变化实现图像隐写的检测,因此本文算法可以获得对通用盲检测算法的抵抗能力.实验结果表明,本文算法在抵抗小波高阶统计量分析、直方图特征函数质心等盲检测算法能力方面,优于LSB匹配、像素值差分等隐写算法.     

Change Detection based on Difference Image and Energy Moments in Remote Sensing Image Monitoring

Permanent control of environment by using remote sensing images requires effective techniques. Two new methods for remote sensing image change detection are proposed. The first method is based on the notion of difference image and image histograms. A complementary pair of images is proposed as the main presentation of a difference image which allows automatic separation of the changes of ground objects without loss or distortion. The use of the histograms in accordance with variations of image brightness (increasing and decreasing) provides opportunities for the assessment and experimental verification of existing approaches in the selection of automatic detection thresholds. The second method for change detection is based on energy moments for image rows and/or columns. It allows one to find image changes even in one pixel and differs from the existed methods by a more simple algorithm and possibility to extract even small changes. The proposed image representation can be considered as an integral feature of the whole image. The methods have been tested in real images. Comparing to start-of-the-art methods, our methods can detect changes in real-time with high accuracy when deployed on a standard computer.     

A robust watermarking algorithm based on QR factorization and DCT using quantization index modulation technique

We propose a robust digital watermarking algorithm for copyright protection.A stable feature is obtained by utilizing QR factorization and discrete cosine transform(DCT) techniques,and a meaningful watermark image is embedded into an image by modifying the stable feature with a quantization index modulation(QIM) method.The combination of QR factorization,DCT,and QIM techniques guarantees the robustness of the algorithm.Furthermore,an embedding location selection method is exploited to select blocks with small modifications as the embedding locations.This can minimize the embedding distortion and greatly improve the imperceptibility of our scheme.Several standard images were tested and the experimental results were compared with those of other published schemes.The results demonstrate that our proposed scheme can achieve not only better imperceptibility,but also stronger robustness against common signal processing operations and lossy compressions,such as filtering,noise addition,scaling,sharpening,rotation,cropping,and JPEG/JPEG2000 compression.     

High capacity lossless data embedding technique for palette images based on histogram analysis

Recently data embedding over images has drawn tremendous interest, using either lossy or lossless techniques. Although lossy techniques can allow large hiding capacity, host image cannot be recovered with high fidelity. Some applications require exact recovery of the host image, i.e. in medicine patient data can be embedded without affecting the medical image. In general lossless data hiding techniques suffer from limited capacity as the host image should be kept intact. In this paper a lossless embedding technique is proposed. In this technique image histograms are analyzed to identify the embedding capacity of different image types. Histogram maxima and minima are used in embedding capacity estimation. The proposed technique gives hiding capacity that can reach up to 50% of the host image size for images with large homochromatic regions (cartoons-like). In fact, our study showed that the embedding capacity is not only affected by the host image size but also by its histogram distribution. The data embedding and extraction is performed using simple processing operations that can save on power consumption for wireless devices.     

Quadruple Difference Expansion-Based Reversible Data Hiding Method for Digital Images

ABSTRACT

Lossless data hiding is a special type of data hiding technique that guarantees not only the secret data but also the ability of cover media to be reconstructed without any distortion. A latest lossless data hiding technique is proposed by Hong Lin Jin's that is based on hiding only one data bit in the spatial domain in gray-level image. However, this method uses double difference expansion to embed bits which results in a small embedding capacity. For this purpose, we propose an improved algorithm with the potential of increasing the payload capacity and maintaining good image quality. The proposed improved algorithm is characterized by two aspects. First, the proposed improved reversible data hiding scheme is enhanced to exhibit data hiding in color palette images. Second, the embedding level is improved by using quadruple difference expansion to guarantee the embedding of 2-bit data into color images. Experiments of the proposed improved method have been conducted over several well-known test images. The results show that the proposed improved method significantly improves the embedding capacity over Hong Lin Jin's scheme by the range of 15–35% for grayscale images and 20–46% for color images while still maintaining the quality of the stego-images.     

A data embedding scheme for color images based on genetic algorithm and absolute moment block truncation coding

Recently, embedding a large amount of secret data into gray-level and color images with low distortion has become an important research issue in steganography. In this paper, we propose a data embedding scheme by using a well-known genetic algorithm, block truncation code and modification direction techniques to embed secret data into compression codes of color images to expand the variety of cover media. In the scheme, the common bitmap generation procedure of GA-AMBTC has been modified to speed up the hiding procedure. Two embedding strategies are proposed to hide secret data into the common bitmap and the quantization values in each block of the cover image. Experimental results confirm that the proposed scheme can provide high data capacity with acceptable image quality of the stego-images. Moreover, the compression ratio of the scheme is exactly the same as that of GA-AMBTC so that attackers cannot detect any trace of hidden data from the size of the modified compressed result.     

High capacity data hiding based on interpolated image

We investigate the use of parabolic interpolation in data hiding and propose a novel data hiding algorithm with high capacity based on interpolated image. Specifically, the proposed algorithm creates an interpolated image from input image by parabolic interpolation, and embeds secret bits into interpolated pixels in terms of the relation between the interpolated value and the mean value. Ten standard benchmark images are taken as test images for validating efficiency of our algorithm. The results illustrate that our algorithm has better performances than some popular data hiding methods in embedding capacity and visual quality with respect to PSNR and SSIM.     

基于差值直方图的简单高效可逆信息隐藏方法

提出一种基于图像相邻像素差值直方图的可逆信息隐藏方法,运用该方法同时提高了信息嵌入容量和嵌入信息后的图像质量。首先对原始图像进行分块、扫描并求取差值直方图,然后在分块图像的差值直方图中选取最高的两个峰值点并向两个方向移位来产生空缺用于信息嵌入。算法充分利用了自然图像的相邻相似特性,同时提高了基于直方图的信息隐藏方案的峰值点数目和峰值点高度,从而大大提高信息嵌入容量。此外,算法在信息嵌入前对待嵌入信息的预处理使得在嵌入同样容量的信息时,对载体图像的影响更小,具有更好的嵌入后图像质量。     

弥散加权图像的鲁棒水印算法研究

目的 弥散加权成像（DWI）作为一种新型医学影像成像技术,已逐渐成为诊断心脏、大脑、肾脏、肝脏等器官中的神经、纤维组织病变的重要方法和手段。与传统的核磁共振（MRI）成像相比,通过使用不同的扩散方向矢量,在不同的扩散参数下,DWI图像呈现的灰度信息也有所不同。目前尚无相关文献提出针对DWI图像版权信息进行有效保护的相关研究。方法 为有效保护病人的DWI图像版权信息,提出一种基于DWI图像的整数小波变换域（IWT）统计直方图的鲁棒水印算法。该算法首先通过最大类间方差分割算法和面积控制阈值获取指定断层中带有弥散梯度方向图像的前景区域,作为待嵌入区域。对待嵌入区域使用整数小波变换获取低频子带系数,利用固定步长对低频子带系数进行统计,生成统计直方图,对统计直方图相邻簇的比值关系进行修改用于水印嵌入;最后提出DWI表观系数与弥散张量成像（DTI）中弥散张量值的可逆关系构建可逆密钥,利用该密钥将嵌入水印后的DWI图像再次加密,从而有效保护DWI图像的版权信息。结果 实验结果表明该算法引入的水印信息对DWI图像中的纤维参数改变量极小。在纤维方向和平均弥散程度改变个数上,本文算法与文献方法相比,分别降低了100多个和30多个;在可视质量上,本文算法提高约8 dB。在高斯噪声、小角度旋转等攻击中,本文算法能够提供较高的提取水印准确率。结论 本文算法对医生诊断的影响在可接受的范围内,且在感兴趣区域遭受各种常见攻击时,具有较高的安全性和鲁棒性。     

A steganography embedding method based on edge identification and XOR coding

In this paper, we present a novel image steganography algorithm that combines the strengths of edge detection and XOR coding, to conceal a secret message either in the spatial domain or an Integer Wavelet Transform (IWT) based transform domain of the cover image. Edge detection enables the identification of sharp edges in the cover image that when embedding in would cause less degradation to the image quality compared to embedding in a pre-specified set of pixels that do not differentiate between sharp and smooth areas. This is motivated by the fact that the human visual system (HVS) is less sensitive to changes in sharp contrast areas compared to uniform areas of the image. The edge detection method presented here is capable of estimating the exact edge intensities for both the cover and stego images (before and after embedding the message), which is essential when extracting the message. The XOR coding, on the other hand, is a simple, yet effective, process that helps in reducing differences between the cover and stego images. In order to embed three secret message bits, the algorithm requires four bits of the cover image, but due to the coding mechanism, no more than two of the four bits will be changed when producing the stego image. The proposed method utilizes the sharpest regions of the image first and then gradually moves to the less sharp regions. Experimental results demonstrate that the proposed method has achieved better imperceptibility results than other popular steganography methods. Furthermore, when applying a textural feature steganalytic algorithm to differentiate between cover and stego images produced using various embedding rates, the proposed method maintained a good level of security compared to other steganography methods.     

基础信息保持和细节强化的胸部CT图像增强

目的 清晰的胸部计算机断层扫描（computed tomography,CT）图像有助于医生准确诊断肺部相关疾病,但受成像设备、条件等因素的限制,扫描得到的CT图像质量有时会不尽如人意。因此,本文提出一种简单有效的基于基础信息保持和细节强化的胸部CT图像增强算法。方法 利用多尺度引导滤波器将胸部CT图像分解为一个基础信息层和多个不同尺度的细节层。基于熵的权重将胸部CT图像的多个细节层进行融合,并乘以强化系数进一步增强纹理细节。将强化的细节层和原始的基础信息层重新组合即可生成细节强化的胸部CT图像。通过此种增强方式,本文算法既能显著增强胸部CT图像的纹理细节,又能将大部分原始的基础结构信息保留到增强图像中。结果 为了验证算法的有效性,将本文算法与5种优秀的图像增强算法在由3 209幅胸部CT图像组成的数据集上进行测试评估。定性和定量实验结果表明,本文算法得到的增强图像保持了更多原始胸部CT图像中的基础结构信息,并更显著地强化了其中的纹理细节信息。在定量结果中,本文算法的标准差、结构相似性和峰值信噪比指标值均优于对比的5种方法,相比于性能第2的方法分别提高了4.95、0.16和4.47,即分别提升了5.61%、17.00%和16.17%。此外,本文算法增强一幅CT图像仅消耗0.10 s,有潜力应用于实际的临床诊断中。结论 本文算法可以在保留大量原始结构信息的同时有效强化CT图像的细节信息,有助于医生对患者肺部疾病的精确诊断。本文算法具有较好的泛化能力,可以用于增强其他部位的CT图像和其他模态图像并取得优秀的增强结果。     

基于三分量预测误差差值扩展的可逆数据隐藏

针对传统差值扩展存在过分修改像素值、须嵌入定位图等缺陷,提出了一种新的彩色图像可逆数据隐藏算法。首先利用色彩分量之间的相关性减小差值,并根据扩展方向和差值的符号,用较小的差值扩展量修改部分色彩分量的像素值,扩展方向由可能产生上溢和下溢的像素数量决定;其次采用单向像素值调整的方法避免像素值溢出,并用少量的调整信息代替定位图,嵌入容量大幅提高;最后改进差值直方图平移技术控制嵌入容量和图像失真。提取端根据临界像素值的顺序定位调整的像素位置,用调整信息恢复调整的像素值,在提取信息的同时可无损地恢复原始图像。实验结果表明,所提出的算法在大幅提高嵌入容量的同时,仍能保持较高的图像质量。