无损载体和鲁棒代价结合的JPEG图像鲁棒隐写

目的 传统隐写技术在实际社交网络信道上难以保护秘密信息的完整性。在社交网络中,图像往往经过有损压缩信道进行传输,从而导致隐蔽通信失效。为了保证经过压缩信道传输的载密图像鲁棒性,设计安全鲁棒的隐蔽通信技术具有实际应用价值。基于最小化图像信息损失,本文提出无损载体和鲁棒代价结合的JPEG图像鲁棒隐写。方法 首先,指出构造无损载体能有效维持隐写安全性和鲁棒性的平衡,对经过压缩信道前后的JPEG图像空域像素块进行差分,构造无损载体以确定鲁棒嵌入域;其次,通过对离散余弦变换（discrete cosine transform,DCT）系数进行“±1”操作,并计算空域信息在压缩传输前后的损失,设计衡量DCT系数抗压缩性能的鲁棒代价;同时,验证在低质量因子压缩信道下鲁棒代价更能区分DCT系数的鲁棒能力,最后,利用校验子格编码（syndrome-trellis code,STC）,结合无损载体和鲁棒代价对秘密信息进行嵌入。结果 实验在BossBase1.01图像库上进行对比实验,相比于传统JPEG隐写技术,构造无损载体作为嵌入域能有效地将信息平均提取错误率降低24.97%,图像的正确提取成功率提高了21.35%;在此基础上,鲁棒代价进一步将信息平均提取错误率降低1.05%,将图像的正确提取成功率提高16.12%,验证了本文方法显著提高了隐写抗压缩性能。与J-UNIWARD （JPEG universal wavelet relative distortion）、JCRISBE （JPEG compression resistant solution with BCH code）和AutoEncoder （autoencoder and adaptive BCH encoding）3种现有典型隐写方法相比,提出的方法信息平均提取错误率分别降低了95.78%、93.17%和87.38%,图像的正确提取成功率为另外3种隐写方法的86.69倍、30.74倍和4.13倍。图像视觉质量逼近传统隐写方法,并保持较好的抗检测性。结论 本文提出的抗低质量因子JPEG压缩鲁棒隐写方法,获得的中间图像在经过压缩信道后,具有较强的抗压缩性和抗检测性,并保持较高的图像质量。     

数字图像鲁棒隐写综述

随着智能设备和社交网络的飞速发展,通过网络传输的数字图像成为了实施隐蔽通信的新型重要载体,适应网络信道的图像隐写技术有望成为开放网络环境下可靠、隐蔽传递信息的一种重要方式。然而,数字图像通过Facebook、Twitter、微信、微博等社交网络传输的过程中,往往会遭受压缩、缩放、滤波等处理,对传统信息隐藏技术在兼顾鲁棒性与抗检测性方面提出了新的挑战。为此,研究者经过多年的努力探索,提出了可抵抗多种图像处理攻击和统计检测的新型鲁棒隐写技术。本文结合网络有损信道中隐蔽通信应用需求,对现有的数字图像鲁棒隐写技术进行综述。首先简要介绍本领域的研究背景,并从图像水印和隐写两方面对图像信息隐藏技术的基本概念、相关技术和发展趋势进行了简要总结。在此基础上,将图像鲁棒隐写技术的研究架构分为载体图像选择、鲁棒载体构造、嵌入代价度量、嵌入通道选择、信源/信道编码以及应用安全策略等方面,并分别对相关方法的基本原理进行了归纳和阐述。随后,对具有代表性的相关方法进行了对比测试,并结合应用场景需求给出了推荐的鲁棒隐写方法。最后,指出了数字图像鲁棒隐写技术有待进一步研究解决的问题。     

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.     

Remote sensing image enhancement based on the combination of adaptive nonlinear gain and the PLIP model in the NSST domain

To enhance image detail and contrast effectively, we present a novel enhancement method for remotely sensed images. This method is based on the combination of adaptive nonlinear gain and the parameterized logarithmic image processing model (PLIP) in the nonsubsampled shearlet transform (NSST) domain. The algorithm works in several stages by deconstructing the image into low- and high-frequency components, applying different functions to each set of frequency components, and then applying further enhancement functions to the reconstructed image. The experimental results show that the proposed method performs well in terms of definition gain, the contrast improvement index (CII) and the measure of enhancement by entropy (EMEE) when compared to several state-of-the-art image enhancement algorithms, including the nonsubsampled contourlet transform (NSCT) with fuzzy field enhancement, the NSCT with unsharp masking, the feature-linking model, linking synaptic computation for image enhancement and improved fuzzy contrast in the NSST domain.

     

No-reference image quality assessment based on localized discrete cosine transform for JPEG compressed images

In this paper, we propose a new no-reference image quality assessment for JPEG compressed images. In contrast to the most existing approaches, the proposed method considers the compression processes for assessing the blocking effects in the JPEG compressed images. These images have blocking artifacts in high compression ratio. The quantization of the discrete cosine transform (DCT) coefficients is the main issue in JPEG algorithm to trade-off between image quality and compression ratio. When the compression ratio increases, DCT coefficients will be further decreased via quantization. The coarse quantization causes blocking effect in the compressed image. We propose to use the DCT coefficient values to score image quality in terms of blocking artifacts. An image may have uniform and non-uniform blocks, which are respectively associated with the low and high frequency information. Once an image is compressed using JPEG, inherent non-uniform blocks may become uniform due to quantization, whilst inherent uniform blocks stay uniform. In the proposed method for assessing the quality of an image, firstly, inherent non-uniform blocks are distinguished from inherent uniform blocks by using the sharpness map. If the DCT coefficients of the inherent non-uniform blocks are not significant, it indicates that the original block was quantized. Hence, the DCT coefficients of the inherent non-uniform blocks are used to assess the image quality. Experimental results on various image databases represent that the proposed blockiness metric is well correlated with the subjective metric and outperforms the existing metrics.

     

基于DCT域相关性的非对称JPEG隐写

目前用于JPEG隐写的失真代价函数对同一DCT系数的加一与减一操作分配相同的代价值。但由于JPEG图像中DCT域的相关性,加一与减一修改对图像内容的影响不同,因此其对应的代价值也理应不同。文中基于DCT域相关性,提出了一种适用于JPEG隐写的通用代价值优化方法,主要考虑JPEG图像中相邻DCT块相同位置上系数的相关性。利用八邻域块中相同位置的DCT系数求平均的方法给出当前DCT系数的预测值。对现有的JPEG失真代价函数,按照向预测值靠拢的原则区分加一和减一的代价值。经过调整的代价值能引导隐写修改后的DCT系数最大程度地向预测值靠拢,增强DCT域相关性,从而提高隐写的安全性。该方法可以与任何现有的JPEG隐写失真代价函数结合使用。实验表明,所提方法几乎不增加原始算法的时间复杂度,同时能有效提高现有JPEG隐写方法的安全性,可使当前隐写分析工具的测试误差平均提升2.4%。     

图像隐写分析中小波系数相关性的研究与应用*

提出了一种针对空域图像隐写的盲检测方法。利用互信息分析秘密信息嵌入对图像小波系数在尺度方向和空间方向相关性的影响,使用马尔可夫模型挖掘小波系数层内和层间相关性,提取转移概率矩阵作为特征。针对LSB匹配和随机调制隐写算法的实验表明,此方法能有效检测未经JPEG压缩过的含密图像,相比现有空域盲检测方法,对低嵌入率含密图像的正确检测率提高约8%14% 。     

Stego key searching for LSB steganography on JPEG decompressed image基于 JPEG 解压缩图像的 LSB 隐写密钥搜索方法

At present, steganalysis research focuses on detecting the existence of a hidden message. However, extracting the hidden information, i.e., an extracting attack, is crucial in obtaining effective evidence in computer forensics. Due to the difficulty of an extracting attack, research in this field is limited. In steganography with a stego key, an extracting attack is equivalent to recovering the stego key. In this paper we study a method for recovering the stego key in least significant bit (LSB) steganography with a decompressed JPEG image as the cover image. Firstly, the recovery of the stego key is translated into a cryptanalysis problem for a sequential cipher. The method for recovering the stego key is based on estimating the modification positions. The minimum size of the data used to recover the stego key successfully is discussed. Secondly, when a decompressed JPEG image is used as the cover image, the probability of recovering the cover pixels using recompression is discussed. Recompression is used to compute the error of the estimated sequence. Finally, an algorithm to recover the stego key in LSB steganography with a decompressed JPEG image as the cover image is proposed. The experimental results for the steganographic software, Hide and Seek 4.1 and its variant, which is a typical representative of LSB steganography, show that the proposed method can successfully recover the stego key in LSB replacement and LSB matching, i.e., the extracting attack is successful, and it outperforms three previous methods in terms of computational complexity.     

基于Hilbert-Huang变换的JPEG2000隐写分析

实现了针对由Su等人提出的JPEG2000 Lazy—mode隐写术的可靠检测．在理论和实验分析的基础上,文章揭示了由Lazy—mode隐写术生成的掩密图像,其子带代码块噪声方差序列的振荡特征异于非掩密含噪图像的子带代码块噪声方差序列．因此,此文隐写检测算法的关键在于针对这两种子带代码块噪声方差序列进行序列分析,提取它们内在的振荡特征差异．在序列分析中,通过引入Hilbert—Huang变换,对噪声方差序列进行经验模式分解,构建了基于Hilbert谱的特征向量．实验表明,基于该特征向量的支持向量机（SVM）分类器能以平均90．6％的准确率识别掩密图像．根据检索,目前尚未有对JPEG2000Lazymode隐写术进行成功分析的报道,因此,该文具有重大意义．     

Dither modulation based adaptive steganography resisting jpeg compression and statistic detection

In order to improve the JPEG compression resistant performance of the current steganogrpahy algorithms resisting statistic detection, an adaptive steganography algorithm resisting JPEG compression and detection based on dither modulation is proposed. Utilizing the adaptive dither modulation algorithm based on the quantization tables, the embedding domains resisting JPEG compression for spatial images and JPEG images are determined separately. Then the embedding cost function is constructed by the embedding costs calculation algorithm based on side information. Finally, the RS coding is combined with the STCs to realize the minimum costs messages embedding while improving the correct rates of the extracted messages after JPEG compression. The experimental results demonstrate that the algorithm can be applied to both spatial images and JPEG images. Compared with the current S-UNIWARD steganography, the message extraction error rates of the proposed algorithm after JPEG compression decrease from about 50 % to nearly 0; compared with the current JPEG compression and detection resistant steganography algorithms, the proposed algorithm not only possesses the comparable JPEG compression resistant ability, but also has a stronger detection resistant performance and a higher operation efficiency.     

基于DCT和SVD的图像哈希水印算法

为使频域水印技术更好地应用于数字图像的版权保护,提出一种基于离散余弦变换和奇异值分解相结合的图像哈希水印算法。利用DWT变换提取载体图像的低频系数矩阵构造水印;对载体图像进行分块DCT变换,提取每个子块的低频系数;对低频系数所组成的矩阵进行SVD变换,在对角阵上嵌入水印;对频域系数进行逆变换得到含水印图。将已有算法和当前所提出的算法进行对比,实验结果表明,所提水印算法具有良好的不可感知性和鲁棒性。     

生成对抗网络驱动的图像隐写与水印模型

目的 图像信息隐藏包括图像隐写术和图像水印技术两个分支。隐写术是一种将秘密信息隐藏在载体中的技术,目的是为了实现隐秘通信,其主要评价指标是抵御隐写分析的能力。水印技术与隐写术原理类似,但其是通过把水印信息嵌入到载体中以达到保护知识产权的作用,追求的是防止水印被破坏而尽可能地提高水印信息的鲁棒性。研究者们试图利用生成对抗网络（generative adversarial networks,GANs）进行自动化的隐写算法以及鲁棒水印算法的设计,但所设计的算法在信息提取准确率、嵌入容量和隐写安全性或水印鲁棒性、水印图像质量等方面存在不足。方法 本文提出了基于生成对抗网络的新型端到端隐写模型（image information hiding-GAN,IIH-GAN）和鲁棒盲水印模型（image robust blind watermark-GAN,IRBW-GAN）,分别用于图像隐写术和图像鲁棒盲水印。网络模型中使用了更有效的编码器和解码器结构SE-ResNet（squeeze and excitation ResNet）,该模块根据通道之间的相互依赖性来自适应地重新校准通道方式的特征响应。结果 实验结果表明隐写模型IIH-GAN相对其他方法在性能方面具有较大改善,当已知训练好的隐写分析模型的内部参数时,将对抗样本加入到IIH-GAN的训练过程,最终可以使隐写分析模型的检测准确率从97.43%降低至49.29%。该隐写模型还可以在256×256像素的图像上做到高达1 bit/像素（bits-per-pixel）的相对嵌入容量;IRBW-GAN水印模型在提升水印嵌入容量的同时显著提升了水印图像质量以及水印提取正确率,在JEPG压缩的攻击下较对比方法提取准确率提高了约20%。结论 本文所提IIH-GAN和IRBW-GAN模型在图像隐写和图像水印领域分别实现了领先于对比模型的性能。     

面向可逆图像处理网络的可证安全自然隐写

目的 自然隐写是一种基于载体源转换的图像隐写方法,基本思想是使隐写后的图像具有另一种载体的特征,从而增强隐写安全性。但现有的自然隐写方法局限于对图像ISO(International Standardization Organization)感光度进行载体源转换,不仅复杂度高,而且无法达到可证安全性。为了提高安全性,本文结合基于标准化流的可逆图像处理模型,在隐空间完成载体源转换,同时通过消息映射的设计做到了可证安全的自然隐写。方法 利用目前发展迅速的基于可逆网络的图像处理方法将图像可逆地映射到隐空间,通过替换使用的隐变量完成载体源的转换,从而避免对原始图像复杂的建模。同时,改进了基于拒绝采样的消息映射方法,简单地从均匀分布中采样以获得需要的条件分布,高效地将消息嵌入到隐变量中,并且保证了嵌入消息后的分布与原本使用的分布一致,从而实现了可证安全的自然隐写。结果 针对图像质量、隐写容量、消息提取准确率、隐写安全性和运行时间进行了实验验证,结果表明在使用可逆缩放网络和可逆去噪网络时能够在每个像素值上平均嵌入5.625 bit消息,且具有接近99%的提取准确率,同时隐写分析网络SRNet(st...     

基于DCT和SVD联合的数字水印算法

离散余弦变换(discrete cosine transform,DCT)和奇异值分解(singular value decomposition,SVD)都可以作为数字水印算法有效的工具,现提出了一种基于离散余弦变换和奇异值分解联合的数字水印算法,先对整幅图像运用离散余弦变换,将离散余弦系数按照"之"字型顺序绘制成4个象限,然后再对每个象限运用奇异值分解方法.实验结果表明本算法具有很好的稳健性,在经过了一般的信号处理操作和JPEG压缩后,嵌入的水印能被可靠的提取和检测.     

基于SVD和Radon变换的抗旋转攻击盲水印算法

为了提高传统基于奇异值变换（SVD）的数字水印抗几何攻击能力,提出一种在小波变换域将Radon变换和奇异值变换相结合的抗旋转攻击鲁棒性水印算法。将宿主图像进行小波变换,对变换后的低频子带进行奇异值分解,将经过仿射变换置乱后的二值水印图像嵌入到奇异值中。在水印嵌入操作上采用了奇偶量化嵌入算法从而实现了二值水印图像在水印检测时的盲提取;同时在水印检测之前,利用Radon变换检测算法对待检测图像进行几何校正,然后提取水印信息。实验结果表明,该算法对于噪声感染、滤波、JPEG压缩等常规信号处理的鲁棒性优于传统的基于SVD的数字水印算法,同时对于旋转几何变换具有很好的鲁棒性。     

一种JPEG图像的DCT域隐写分析方法

提出了一种基于JPEG图像DCT系数差分矩阵统计特征的隐写分析方法。该算法保留了以往算法选用的DCT系数水平和竖直方向的差分矩阵相关特征,通过增加其主副对角线方向上差分矩阵来提取和计算特征向量,进而利用SVM分类器进行分类。实验结果表明,该方法能够有效地对JPEG图像进行检测,并且具有较高的检测正确率。     

Fuzzy intensification operator based contrast enhancement in the compressed domain

With the increasing sizes of high resolution images, their storage and processing directly in the compressed domain has significantly gained importance. Algorithms for compressed domain image processing provide a powerful computational alternative to classical (pixel level) based implementations. While linear algorithms can be applied straightforward to the JPEG compressed images, this is not the case for nonlinear image processing, as for example contrast enhancement algorithms. In this paper a new implementation in the compressed domain of a very efficient contrast enhancement, based on fuzzy set modeling and on a fuzzy intensification operator, is presented. The fuzzy set parameters are adaptively chosen by analyzing the statistics of the image data in the compressed domain, in order to optimally enhance the image contrast. The nonlinear enhancement procedure requires a grey level threshold, for which an adaptive implementation, taking into account the frequency content of each coefficient block in the DCT (Discrete Cosine Transform) encoded JPEG image is proposed. This guarantees the optimal quality at minimum computational cost. The experimental results for a set of various contrast images validate the good performance and functionality of the proposed implementation.     

基于NSST域的自适应区域和SCM相结合的多聚焦图像融合

为了提高多聚焦图像的融合效果,结合多源图像之间的共享相似性,提出了一种基于非下采样Shearlet变换(Nonsubsampled Shearlet Transform,NSST)域的自适应区域与脉冲发放皮层模型(Spiking Cortical Model,SCM)结合的新型图像融合算法。首先用NSST分解源图像,然后计算边缘能量(Energy Of Edge,EOE),在自适应区域用投票加权法融合低频系数,高频系数由边缘能量作为输入的SCM点火图融合,最后通过逆NSST获得该融合图像。该算法既可以很好地保持源图像的信息,又可以抑制在变换域因非线性运算产生的像素失真。实验结果表明,该方法优于最新的变换域和脉冲耦合神经网络(Pulse Coupled Neural Network,PCNN)融合方法。     

基于马尔可夫链理论的隐写检测算法

基于马尔可夫链（Markov Chain,MC）理论,提出了一种新的通用隐写检测算法。根据图像邻域相关的性质构造马尔可夫链,提取其经验转移矩阵的对角线元素作为特征向量,构造了一个新的判决函数作为检测秘密信息是否存在的依据。基于Matlab7.0平台,对全局LSB、DCT和DWT的隐写进行了检测实验。根据实验结果对算法进行了改进,使检测效果更优。结果证明：该算法的综合性能优于普通的检测算法。     

基于纹理和高斯密度特征的图像检索算法

直接从DCT域中提取图像的特征是提高图像的检索效率的方法.直接从压缩域中提取图像的高斯密度,即计算图像在8个方向上的分段累加值,形成一个8*4的二维向量,再结合图像的纹理特征来进行图像检索.为了验证算法的可行性,建立了10000幅图像的图像库.实验结果表明,该方法能够准确地检索出目标图像,有效地提高了图像检索的精度和速度.