数字图像无损信息隐藏方法研究
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摘要
自古以来，人类的信息交流方式经历了从语言、文字到数字化多媒体的历史变迁。今天，数字图像以其直观、生动和跨语言等优势，广泛地应用于国民经济的各个领域。然而，飞速发展的信息技术使得数字图像的信息安全面临严峻挑战。针对这一问题，信息隐藏作为一门新兴的交叉学科应运而生。传统的信息隐藏方法在将隐秘信息嵌入到宿主图像的过程中，往往给图像内容造成永久性失真，这成为它在医学、遥感及法庭证据等应用的重要障碍。因此，数字图像无损信息隐藏成为近年来多媒体信息安全领域的研究热点。
本文针对无损信息隐藏方法所面临的挑战性问题，分别从可逆性、不可感知性、嵌入模型及鲁棒性等方面深入探索了数字图像无损信息隐藏的解决之道。本文的主要工作可概括如下：
(1)提出了基于优化策略的提升无损信息隐藏方法。首先通过设计优化策略进行嵌入区域的选择，然后利用嵌入机制与参数模型实现了水印与宿主图像的无失真恢复，从而有效克服了基于直方图分布约束方法存在的可逆性不稳定问题。
(2)考虑到不可感知性在信息隐藏中的重要作用，基于直方图旋转的嵌入模型提出了一种内容自适应的可信无损信息隐藏方法。首先利用像素调整策略对宿主图像进行预处理，有效避免了嵌入过程中的像素溢出，提高了水印图像的视觉质量；并结合人类视觉系统的感知特性，利用亮度掩膜模型进一步优化水印嵌入强度，实现了内容自适应的可信无损水印嵌入与提取。
(3)针对因图像灰度直方图多样性而导致的水印嵌入稳定性差、适应性弱等不足，提出了一种基于统计特征的邻域选择无损信息隐藏方法。首先利用广义统计量直方图良好的分布特性有效克服了灰度直方图间的差异；在此基础上，结合直方图邻域选择构建稳定的可伸缩式嵌入区域，实现了容量的灵活控制，增强了稳定性与适应性。同时，加密与无损压缩技术的使用有效解决了边信息存储问题，提高了无损信息隐藏的安全性。
(4)鉴于实际应用环境中隐秘攻击对水印图像的降质影响，将广义统计量直方图平移嵌入与聚类算法相结合，提出了基于聚类的空域鲁棒无损信息隐藏方法。在深入分析水印图像嵌入区域分布特性的基础上，利用k-means聚类对其进行动态划分，增强了水印抗非恶意处理的鲁棒性。实验结果表明该方法在鲁棒性、不可感知性、容量等方面均具有良好的性能，体现了广泛的适应性和良好的稳定性。
(5)为了进一步增强水印抗非恶意处理的鲁棒性，结合高通子带小波系数分布特征，提出了基于视觉感知的频域鲁棒无损信息隐藏方法。利用属性启发式像素调整策略避免了像素溢出，在此基础上，通过直方图平移与聚类算法实现了水印的嵌入与提取，并设计了增强型像素掩膜模型来优化水印嵌入强度，实现了水印不可感知性与鲁棒性的有效折衷。
综上所述，本文将模式分类、计算机视觉等领域的基础理论引入到无损信息隐藏的研究中，所提出的五种新方法有效克服了现有方法存在的诸如可逆性不稳定、视觉质量差、鲁棒性弱等问题，为解决无损信息隐藏面临的挑战性问题提供了新思路。
Throughout history, great changes have taken place in the way of humancommunications from language, writing to digital multimedia. The past years havewitnessed an explosion in the availability of digital images in different fields due tosuch factors as direct viewing, vividness and super-language. However, the fastdevelopment of the information technology has brought to forefront security concernswith the use of digital images. Recently, data hiding has been proposed and popularlyemployed as an effective way to solve this problem. Usually, traditional data hidingmethods cause irreversible distortion of image contents during the embedding process,which is an obstacle to their usage in such important applications as medical diagnosis,remote sensing and law enforcement. Therefore, lossless data hiding (LDH) of digitalimages has attracted increasing attention for copyright protection in multimediainformation security.
To target the challenging problems of LDH, the paper focuses on reversibility,imperceptibility, embedding model and robustness of LDH and makes an extensivestudy of lossless data hiding for digital images. The main contents of this paper aresummarized as follows.
(1) An improved reversibility LDH method is proposed based on the optimalstrategy. First, the stable regions for watermark embedding are picked up by the optimalstrategy. Secondly, a novel embedding mechanism together with a parameter model isdesigned to tune the embedding process. Based on this, the proposed method caneffectively overcome the unstable reversibility of the histogram distribution constraintmethod.
(2) By taking the importance of imperceptibility to data hiding into consideration, acontent-adaptive reliable LDH method is designed on the basis of the histogramrotation-based embedding model. The host images are first preprocessed by the pixeladjustment strategy to avoid both the overflow and underflow problems of pixels, andthus to improve the visual quality of watermarked images. Thereafter, inspired by theperceptual characteristics of the human visual system, the watermark strength isoptimized with the help of the luminance masking model, leading to thecontent-adaptive, reliable and lossless data hiding.
(3) Due to the diversity of grayscale histograms of different images, watermark embedding suffers from poor stability and adaptability. Therefore, we develop a novelframework for LDH using statistical characteristics and histogram-based neighborhoodselection. The generalized statistical quantity histogram (GSQH) is first employed todeduce the diversity of grayscale histograms. Based on this, the stable and tunableembedding regions are constructed by combining the GSQH and histogram-basedneighborhood selection, leading to flexible capacity control as well as improvement ofstability and adaptability. Moreover, the encryption and lossless compression techniquesare utilized to solve the problem of side information storage, which is helpful toenhance security of the proposed method.
(4) In practical applications, watermarked images may be degraded byunintentional attacks to some extent. Aiming at such an issue, we propose a robustlossless data hiding (RLDH) method, which incorporates the merits of GSQH andclustering algorithms. Based on the investigation of the distribution of embeddingregions in watermarked images, the k-means clustering algorithm is adopted to dealwith their dynamic division to improve the robustness. Extensive experimental resultshave demonstrated the superiority of the proposed method from the aspects ofrobustness, imperceptibility, capacity, adaptability and stability.
(5) To further improve robustness against unintentional attacks, a novel RLDHmethod based on visual perceptual models in the wavelet domain is proposed, whichemploys the statistical characteristics of wavelet coefficients in high-pass sub-bands.The property inspired pixel adjustment is first utilized to solve both the overflow andunderflow problems of pixels. Following this, watermark embedding and extraction isachieved by incorporating the histogram shifting and clustering techniques. Furthermore,we design an enhanced pixel-wise masking model to optimize watermark strength,leading to a good tradeoff between imperceptibility and robustness.
In this paper, we integrate the basic theories of pattern classification and computervision into LDH and propose five novel LDH methods. By effectively overcoming thedrawbacks of the existing methods including unstable stability, poor visual quality androbustness, the proposed ones will be able to successfully address the challengingproblems facing the LDH.
本文针对无损信息隐藏方法所面临的挑战性问题，分别从可逆性、不可感知性、嵌入模型及鲁棒性等方面深入探索了数字图像无损信息隐藏的解决之道。本文的主要工作可概括如下：
(1)提出了基于优化策略的提升无损信息隐藏方法。首先通过设计优化策略进行嵌入区域的选择，然后利用嵌入机制与参数模型实现了水印与宿主图像的无失真恢复，从而有效克服了基于直方图分布约束方法存在的可逆性不稳定问题。
(2)考虑到不可感知性在信息隐藏中的重要作用，基于直方图旋转的嵌入模型提出了一种内容自适应的可信无损信息隐藏方法。首先利用像素调整策略对宿主图像进行预处理，有效避免了嵌入过程中的像素溢出，提高了水印图像的视觉质量；并结合人类视觉系统的感知特性，利用亮度掩膜模型进一步优化水印嵌入强度，实现了内容自适应的可信无损水印嵌入与提取。
(3)针对因图像灰度直方图多样性而导致的水印嵌入稳定性差、适应性弱等不足，提出了一种基于统计特征的邻域选择无损信息隐藏方法。首先利用广义统计量直方图良好的分布特性有效克服了灰度直方图间的差异；在此基础上，结合直方图邻域选择构建稳定的可伸缩式嵌入区域，实现了容量的灵活控制，增强了稳定性与适应性。同时，加密与无损压缩技术的使用有效解决了边信息存储问题，提高了无损信息隐藏的安全性。
(4)鉴于实际应用环境中隐秘攻击对水印图像的降质影响，将广义统计量直方图平移嵌入与聚类算法相结合，提出了基于聚类的空域鲁棒无损信息隐藏方法。在深入分析水印图像嵌入区域分布特性的基础上，利用k-means聚类对其进行动态划分，增强了水印抗非恶意处理的鲁棒性。实验结果表明该方法在鲁棒性、不可感知性、容量等方面均具有良好的性能，体现了广泛的适应性和良好的稳定性。
(5)为了进一步增强水印抗非恶意处理的鲁棒性，结合高通子带小波系数分布特征，提出了基于视觉感知的频域鲁棒无损信息隐藏方法。利用属性启发式像素调整策略避免了像素溢出，在此基础上，通过直方图平移与聚类算法实现了水印的嵌入与提取，并设计了增强型像素掩膜模型来优化水印嵌入强度，实现了水印不可感知性与鲁棒性的有效折衷。
综上所述，本文将模式分类、计算机视觉等领域的基础理论引入到无损信息隐藏的研究中，所提出的五种新方法有效克服了现有方法存在的诸如可逆性不稳定、视觉质量差、鲁棒性弱等问题，为解决无损信息隐藏面临的挑战性问题提供了新思路。
Throughout history, great changes have taken place in the way of humancommunications from language, writing to digital multimedia. The past years havewitnessed an explosion in the availability of digital images in different fields due tosuch factors as direct viewing, vividness and super-language. However, the fastdevelopment of the information technology has brought to forefront security concernswith the use of digital images. Recently, data hiding has been proposed and popularlyemployed as an effective way to solve this problem. Usually, traditional data hidingmethods cause irreversible distortion of image contents during the embedding process,which is an obstacle to their usage in such important applications as medical diagnosis,remote sensing and law enforcement. Therefore, lossless data hiding (LDH) of digitalimages has attracted increasing attention for copyright protection in multimediainformation security.
To target the challenging problems of LDH, the paper focuses on reversibility,imperceptibility, embedding model and robustness of LDH and makes an extensivestudy of lossless data hiding for digital images. The main contents of this paper aresummarized as follows.
(1) An improved reversibility LDH method is proposed based on the optimalstrategy. First, the stable regions for watermark embedding are picked up by the optimalstrategy. Secondly, a novel embedding mechanism together with a parameter model isdesigned to tune the embedding process. Based on this, the proposed method caneffectively overcome the unstable reversibility of the histogram distribution constraintmethod.
(2) By taking the importance of imperceptibility to data hiding into consideration, acontent-adaptive reliable LDH method is designed on the basis of the histogramrotation-based embedding model. The host images are first preprocessed by the pixeladjustment strategy to avoid both the overflow and underflow problems of pixels, andthus to improve the visual quality of watermarked images. Thereafter, inspired by theperceptual characteristics of the human visual system, the watermark strength isoptimized with the help of the luminance masking model, leading to thecontent-adaptive, reliable and lossless data hiding.
(3) Due to the diversity of grayscale histograms of different images, watermark embedding suffers from poor stability and adaptability. Therefore, we develop a novelframework for LDH using statistical characteristics and histogram-based neighborhoodselection. The generalized statistical quantity histogram (GSQH) is first employed todeduce the diversity of grayscale histograms. Based on this, the stable and tunableembedding regions are constructed by combining the GSQH and histogram-basedneighborhood selection, leading to flexible capacity control as well as improvement ofstability and adaptability. Moreover, the encryption and lossless compression techniquesare utilized to solve the problem of side information storage, which is helpful toenhance security of the proposed method.
(4) In practical applications, watermarked images may be degraded byunintentional attacks to some extent. Aiming at such an issue, we propose a robustlossless data hiding (RLDH) method, which incorporates the merits of GSQH andclustering algorithms. Based on the investigation of the distribution of embeddingregions in watermarked images, the k-means clustering algorithm is adopted to dealwith their dynamic division to improve the robustness. Extensive experimental resultshave demonstrated the superiority of the proposed method from the aspects ofrobustness, imperceptibility, capacity, adaptability and stability.
(5) To further improve robustness against unintentional attacks, a novel RLDHmethod based on visual perceptual models in the wavelet domain is proposed, whichemploys the statistical characteristics of wavelet coefficients in high-pass sub-bands.The property inspired pixel adjustment is first utilized to solve both the overflow andunderflow problems of pixels. Following this, watermark embedding and extraction isachieved by incorporating the histogram shifting and clustering techniques. Furthermore,we design an enhanced pixel-wise masking model to optimize watermark strength,leading to a good tradeoff between imperceptibility and robustness.
In this paper, we integrate the basic theories of pattern classification and computervision into LDH and propose five novel LDH methods. By effectively overcoming thedrawbacks of the existing methods including unstable stability, poor visual quality androbustness, the proposed ones will be able to successfully address the challengingproblems facing the LDH.
引文
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