视觉失真与图像信息隐藏研究
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摘要
信息社会,数据无处不在。如何安全的分发和访问数据,日益重要。信息隐藏能够不引起监听者或者攻击者察觉的将信息隐匿于载体之中,可以不增加通信负担,因而常用于数字版权管理、隐蔽通信和通信数据纠错等方面。信息隐藏必须满足失真约束条件,在水印的不可见性和鲁棒性之间寻求最佳平衡。于是,对于以图像为载体的水印系统,确定合理的视觉失真约束条件至关重要。
本文从视觉感知机理出发,提出了层次化、多通道的视觉失真模型。将视觉失真分为信号层失真和内容层失真,内容层失真又分为纹理失真和结构失真。对这几类失真分别建模,以指导嵌入水印过程中的图像失真控制。为此,本文从四个方面展开研究:
第一,信号层失真。前人研究表明:在两幅图像的刺激下,如果复杂细胞的视觉皮层神经元保持响应相同,那么人脑难以察觉两幅图像之间的差异。据此,本文通过独立子空间分析来模拟神经元响应,提出借助具有能量无损特点的角度量化调制方式将水印嵌入到图像的独立子空间系数中。该方案一方面具有天然的低失真特点,另一方面得益于角度量化机制而能够抵御图像的亮度缩放攻击。
第二,图像的结构与纹理分解。结构和纹理是视觉信号特有的两类成分。结构体现为图像的大尺度的、连贯的边界和轮廓,它表达场景的概貌和景物的形态;纹理反映为零碎的、有规律或无规律的信号抖动区域或者平整的区域,它表现景物表面的质地和光影效果。区分图像的结构和纹理是讨论内容层失真的基础。本文依据稀疏编码原则,改进了基于基元竞争性表达的图像分解方法,以满足后续失真分析和水印算法的需要。
第三,纹理失真。图像纹理一直被认为是适合信息隐藏的区域。纹理感知的心理学研究和计算机图形学的纹理合成研究表明:特征的高阶统计量是纹理辨识的关键因素。然而在实践中,高阶统计量的非线性特性使得它难以作为失真度量指导水印嵌入。为此,本文采用统计特征设计了线性的失真度量近似方法,在其指导下嵌入水印,能够充分的提高水印嵌入强度,从而提高水印的容量。
第四,结构失真。正如几何攻击所展现,图像扭曲有时并不造成明显失真,这说明存在一个关于图像结构的无失真空间。利用这个空间可以辅助嵌入水印,并使水印具有抗合谋攻击的能力。结构感知的相关理论指出,人类对大范围结构信息的感知要优先于或者敏感于局部的结构信息。本文依据大尺度优先原则来定义图像的结构失真,并充分的扭曲图像,以达到嵌入水印的目的。
本文在提出的视觉失真理论的基础上,改进了图像水印算法的性能。图像水印被纳入到能量最小化的框架之下,成为了在不可见性和鲁棒性二者之间寻求平衡的双目标优化问题。该框架一方面兼顾了纹理失真和结构失真的约束,另一方面统一了扩频调制水印和量化调制水印。改进后的图像水印,既不易被察觉,又能够抵御压缩、滤波、亮度缩放以及合谋攻击,从而提高了水印容量。本文提出的视觉失真理论对于其它视觉创意应用也有指导意义。这些应用包括图像补全、融合、修复等编辑工作。
Data is ubiquitous in information society. Secure distribution and access of data become increasingly important. Information hiding, which can embed messages into cover works without arousing the attention from eavesdroppers or attackers, dose not increase the payload of communication, and hence is used for digital rights management, covert communication, error resilient and etc. Information hiding must satisfy the distortion constraint and seek a tradeoff between the invisibility and the robustness of watermark. Therefore, it is crucial for image-based-watermarking to determine a reasonable visual distortion constraint.
Starting from the laws of visual perception, this thesis proposes a hierarchical, multi-channel model of visual distortion. The visual distortion is layered into signal distortion and content distortion, and the latter is decomposed into textural distortion and structural distortion. Under the models of all the distortion categories, image distortion can be controlled during watermarking. To this end, this thesis carries out research in four aspects.
At first, signal distortion. The previous studies show that, visual distortion is not perceivable when the response of a complex cell is kept invariant between the stimuli of two images. Therefore, characterizing neural responses by independent subspace analysis, this thesis proposes to embed watermarks into ISA coefficients of the cover image by energy-conserving hiding code of angle quantization index modulation. This approaches presents intrinsically low distortion, and is robust against valumetric attack because of angle quantization.
Secondly, image decomposition into structure and texture. The structure and the texture are the particular components of visual data. The structure, as the sketch of scene and the shape of object, includes large-scale, continuous edges and contours, while texture, as the surface character or illumination effects, contains fragmentary, regulated or unregulated oscillatory regions as well as smooth regions. Discrimination between the structure and the texture is the foundation of discussion on content distortion. According to principles of sparse coding, this thesis improves the image decomposition approach which is based on completive representation by corresponding bases, so as to meet the requirement of distortion analysis and watermarking schemes.
Then, texture distortion. Texture has been regarded as suitable region for information hiding. Psychology research on texture perception and graphical study about texture synthesis show that high order statistics of feature are key clues for texture recognition. However, due to its nonlinearity, high order statistics is hard to guide the distortion metric for watermarking. Therefore, this thesis design a linear proximate distortion metric based on statistical feature, under the guidance of which watermark intensity can be increased efficiently, and watermark capacity too.
At last, structure distortion. As the geometrical attack shown, image warping may incurs no perceptible distortion, which proves the existence of structurally undistorted image space. Within the space, watermark can be hidden and endowed with robustness against collusion attack. The theory on structure perception shows that human is prefer to perceive or more sensible to large-scale structure rather than local one. According to the large-scale priority principle, this thesis defines the structure distortion, and embedding watermarking by image warping.
Based on the proposed theory of visual distortion, this thesis focuses on the improvement of watermarking schemes. Watermarking is united into a framework of energy minimization, and posed as a biobjective optimization problem for a tradeoff between the invisibility and the robustness. In the framework, constraint about texture distortion and structure distortion are both considered, meanwhile, spread spectrum modulation watermark and quantization index modulation watermark are unified. The improved watermarking is both imperceptible and robust against compression, filtering valumetric scaling and collusion attack, and thus offers high capacity. Moverover, the proposed theory of visual distortion is also useful for other visual creative application, including image completion, blending, inpainting and etc.
本文从视觉感知机理出发,提出了层次化、多通道的视觉失真模型。将视觉失真分为信号层失真和内容层失真,内容层失真又分为纹理失真和结构失真。对这几类失真分别建模,以指导嵌入水印过程中的图像失真控制。为此,本文从四个方面展开研究:
第一,信号层失真。前人研究表明:在两幅图像的刺激下,如果复杂细胞的视觉皮层神经元保持响应相同,那么人脑难以察觉两幅图像之间的差异。据此,本文通过独立子空间分析来模拟神经元响应,提出借助具有能量无损特点的角度量化调制方式将水印嵌入到图像的独立子空间系数中。该方案一方面具有天然的低失真特点,另一方面得益于角度量化机制而能够抵御图像的亮度缩放攻击。
第二,图像的结构与纹理分解。结构和纹理是视觉信号特有的两类成分。结构体现为图像的大尺度的、连贯的边界和轮廓,它表达场景的概貌和景物的形态;纹理反映为零碎的、有规律或无规律的信号抖动区域或者平整的区域,它表现景物表面的质地和光影效果。区分图像的结构和纹理是讨论内容层失真的基础。本文依据稀疏编码原则,改进了基于基元竞争性表达的图像分解方法,以满足后续失真分析和水印算法的需要。
第三,纹理失真。图像纹理一直被认为是适合信息隐藏的区域。纹理感知的心理学研究和计算机图形学的纹理合成研究表明:特征的高阶统计量是纹理辨识的关键因素。然而在实践中,高阶统计量的非线性特性使得它难以作为失真度量指导水印嵌入。为此,本文采用统计特征设计了线性的失真度量近似方法,在其指导下嵌入水印,能够充分的提高水印嵌入强度,从而提高水印的容量。
第四,结构失真。正如几何攻击所展现,图像扭曲有时并不造成明显失真,这说明存在一个关于图像结构的无失真空间。利用这个空间可以辅助嵌入水印,并使水印具有抗合谋攻击的能力。结构感知的相关理论指出,人类对大范围结构信息的感知要优先于或者敏感于局部的结构信息。本文依据大尺度优先原则来定义图像的结构失真,并充分的扭曲图像,以达到嵌入水印的目的。
本文在提出的视觉失真理论的基础上,改进了图像水印算法的性能。图像水印被纳入到能量最小化的框架之下,成为了在不可见性和鲁棒性二者之间寻求平衡的双目标优化问题。该框架一方面兼顾了纹理失真和结构失真的约束,另一方面统一了扩频调制水印和量化调制水印。改进后的图像水印,既不易被察觉,又能够抵御压缩、滤波、亮度缩放以及合谋攻击,从而提高了水印容量。本文提出的视觉失真理论对于其它视觉创意应用也有指导意义。这些应用包括图像补全、融合、修复等编辑工作。
Data is ubiquitous in information society. Secure distribution and access of data become increasingly important. Information hiding, which can embed messages into cover works without arousing the attention from eavesdroppers or attackers, dose not increase the payload of communication, and hence is used for digital rights management, covert communication, error resilient and etc. Information hiding must satisfy the distortion constraint and seek a tradeoff between the invisibility and the robustness of watermark. Therefore, it is crucial for image-based-watermarking to determine a reasonable visual distortion constraint.
Starting from the laws of visual perception, this thesis proposes a hierarchical, multi-channel model of visual distortion. The visual distortion is layered into signal distortion and content distortion, and the latter is decomposed into textural distortion and structural distortion. Under the models of all the distortion categories, image distortion can be controlled during watermarking. To this end, this thesis carries out research in four aspects.
At first, signal distortion. The previous studies show that, visual distortion is not perceivable when the response of a complex cell is kept invariant between the stimuli of two images. Therefore, characterizing neural responses by independent subspace analysis, this thesis proposes to embed watermarks into ISA coefficients of the cover image by energy-conserving hiding code of angle quantization index modulation. This approaches presents intrinsically low distortion, and is robust against valumetric attack because of angle quantization.
Secondly, image decomposition into structure and texture. The structure and the texture are the particular components of visual data. The structure, as the sketch of scene and the shape of object, includes large-scale, continuous edges and contours, while texture, as the surface character or illumination effects, contains fragmentary, regulated or unregulated oscillatory regions as well as smooth regions. Discrimination between the structure and the texture is the foundation of discussion on content distortion. According to principles of sparse coding, this thesis improves the image decomposition approach which is based on completive representation by corresponding bases, so as to meet the requirement of distortion analysis and watermarking schemes.
Then, texture distortion. Texture has been regarded as suitable region for information hiding. Psychology research on texture perception and graphical study about texture synthesis show that high order statistics of feature are key clues for texture recognition. However, due to its nonlinearity, high order statistics is hard to guide the distortion metric for watermarking. Therefore, this thesis design a linear proximate distortion metric based on statistical feature, under the guidance of which watermark intensity can be increased efficiently, and watermark capacity too.
At last, structure distortion. As the geometrical attack shown, image warping may incurs no perceptible distortion, which proves the existence of structurally undistorted image space. Within the space, watermark can be hidden and endowed with robustness against collusion attack. The theory on structure perception shows that human is prefer to perceive or more sensible to large-scale structure rather than local one. According to the large-scale priority principle, this thesis defines the structure distortion, and embedding watermarking by image warping.
Based on the proposed theory of visual distortion, this thesis focuses on the improvement of watermarking schemes. Watermarking is united into a framework of energy minimization, and posed as a biobjective optimization problem for a tradeoff between the invisibility and the robustness. In the framework, constraint about texture distortion and structure distortion are both considered, meanwhile, spread spectrum modulation watermark and quantization index modulation watermark are unified. The improved watermarking is both imperceptible and robust against compression, filtering valumetric scaling and collusion attack, and thus offers high capacity. Moverover, the proposed theory of visual distortion is also useful for other visual creative application, including image completion, blending, inpainting and etc.
引文
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