基于Zernike矩的图像哈希和图像内容认证
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摘要
随着互联网和多媒体技术的发展，数字媒体如图像，音频和视频得到了广泛的传播和应用。由于数字媒体可被轻易修改，如何确保媒体内容的完整性和真实性成为信息安全领域的重要课题。受到研究人员广泛关注的图像Hash用一串短小数字序列表征数字图像的本质内容，是一种用于图像主动认证的重要方法。本论文研究稳健图像Hash理论和关键技术，提出几种有效的图像Hash算法。取得的创新成果概括如下：
1．将对象的形状特征用于图像Hash
对象的形状特征具有不随颜色和周围环境变换而变换的特性，据此提出一种基于形状特征的图像Hash。该方法将预处理后图像的Zernike矩的幅度和相位经过量化编码并置乱后生成图像Hash。Hash长度仅有216比特，对旋转等常规处理具有良好的鲁棒性，能较好地区分相似图像和篡改图像。
2．结合保角变换和Zernike矩的图像Hash
利用密钥对图像的亮度分量做伪随机分块，将每块图像规格化为相同尺寸后做保角变换将其映射为圆形再求Zernike矩，然后将幅度和相位连接起来并置乱得到最终的图像Hash。该方法的特点是提取局部特征构成图像Hash，因此Hash长度较长，鲁棒性和唯一性好。图像分块使Hash具备篡改定位的能力。与其他方法相比，该方法具有较低的碰撞率和检错率。
3．检测颜色异常改变的图像Hash
图像的颜色空间从RGB转换为YCbCr，然后将每个分量从矩形映射为圆形，之后将Zernike矩的幅度和相位连接起来并进行置乱最终构成图像Hash。该方法的Hash长度较长，为66个数，但它同时包含了图像的亮度信息和色差信息，因此可以同时检测图像内容和颜色的异常改变。
4．结合显著特征和Zernike矩的图像Hash
以往的图像Hash算法通常仅由图像的局部特征或全局特征构成，它们各自具有不同的优缺点。本论文提出了一种将图像的局部和全局特征结合起来构成图像Hash的方法。局部特征是提取显著区域的位置信息和纹理特征，全局特征提取亮度图像和色差图像的Zernike矩。在特征提取和Hash生成过程中都引入密钥来保证方法的安全性。该方法对保持内容的常规处理具有鲁棒性，同时对局部篡改敏感，因此可以用于图像认证。通过分解图像Hash，可以确定篡改类型并通过显著区域定位图像篡改的位置。
在结论中对上述几种方法的特点进行了比较。
With the development of network and multimedia technique, digital media suchas image, audio and video, are transmitted and applied widely. Digital media can beprocessed easily. Ensuring security and credibility of the media contents has becomean important issue in information security. Many researchers pay more attentions onimage hashing which is an initiative authentication method. Image hashing maps animage to a short binary sequence representing the image’s characteristics. In thisdissertation, we research on the basic theory of image hash and propose several imagehashing methods. The contributions of this dissertation are listed as follows:
1．Image hashing using shape feature
Shape feature of an object is invariant to color and environment. We propose animage hashing method based on Zernike moments. The amplitude and phase ofZernike moments of preprocessed image are quantified, connected and permuted togenerate the image hash. The hash length is216bits. It is robust against thecontent-preserving procession such as rotation. It has good performance ofdifferentiating similar images and forged images.
2．Image hashing based on conformal mapping and Zernike moments
The input image is pseudo-randomly partitioned into blocks, which are resized tobecome squares of a standard size and mapped to a unit circle by conformal mapping.Zernike moments of the circular-shaped “images” are calculated. All amplitudes andphases of the modified Zernike moments are concatenated to form an intermediatehash sequence. The final hash is produced by pseudo-randomly permuting theintermediate hash. This method extracts local information to form image hash, so ithas long hash length, good robustness and uniqueness. Image blocking allows the tampered regions in the image to be correctly located. Compared to some othertechniques, the method has low probabilities of collision and errors.
3．Image hashing for color forgery detection
The color space is transmitted from RGB to YCbCr. Then each component ismapped from rectangle to circle. At last the amplitude and phase of Zernikemoments is connected and permuted to form the final image hash. The hash length is66numbers, which is longer than the first method. But it includes the intensity andcolor information of the image, so this method can be used to detect unusual contentand color changes.
4．Image hashing based on saliency feature and Zernike moments
Many previous image hash schemes are either based on global or local featureswith various advantages and disadvantages. Both global and local features are used inproposing hash sequence. The global features are based on Zernike momentsrepresenting luminance and chrominance characteristics of the image as a whole. Thelocal features include position and texture information of salient regions in the image.Secret keys are introduced in feature extraction and hash construction for security.While being robust against content-preserving image processing, the hash is sensitiveto malicious tampering and therefore applicable to image authentication. Bydecomposing the hashes, the type of image forgery and location of forged areas can bedetermined.
At last these methods are compared in conclusions.
1．将对象的形状特征用于图像Hash
对象的形状特征具有不随颜色和周围环境变换而变换的特性，据此提出一种基于形状特征的图像Hash。该方法将预处理后图像的Zernike矩的幅度和相位经过量化编码并置乱后生成图像Hash。Hash长度仅有216比特，对旋转等常规处理具有良好的鲁棒性，能较好地区分相似图像和篡改图像。
2．结合保角变换和Zernike矩的图像Hash
利用密钥对图像的亮度分量做伪随机分块，将每块图像规格化为相同尺寸后做保角变换将其映射为圆形再求Zernike矩，然后将幅度和相位连接起来并置乱得到最终的图像Hash。该方法的特点是提取局部特征构成图像Hash，因此Hash长度较长，鲁棒性和唯一性好。图像分块使Hash具备篡改定位的能力。与其他方法相比，该方法具有较低的碰撞率和检错率。
3．检测颜色异常改变的图像Hash
图像的颜色空间从RGB转换为YCbCr，然后将每个分量从矩形映射为圆形，之后将Zernike矩的幅度和相位连接起来并进行置乱最终构成图像Hash。该方法的Hash长度较长，为66个数，但它同时包含了图像的亮度信息和色差信息，因此可以同时检测图像内容和颜色的异常改变。
4．结合显著特征和Zernike矩的图像Hash
以往的图像Hash算法通常仅由图像的局部特征或全局特征构成，它们各自具有不同的优缺点。本论文提出了一种将图像的局部和全局特征结合起来构成图像Hash的方法。局部特征是提取显著区域的位置信息和纹理特征，全局特征提取亮度图像和色差图像的Zernike矩。在特征提取和Hash生成过程中都引入密钥来保证方法的安全性。该方法对保持内容的常规处理具有鲁棒性，同时对局部篡改敏感，因此可以用于图像认证。通过分解图像Hash，可以确定篡改类型并通过显著区域定位图像篡改的位置。
在结论中对上述几种方法的特点进行了比较。
With the development of network and multimedia technique, digital media suchas image, audio and video, are transmitted and applied widely. Digital media can beprocessed easily. Ensuring security and credibility of the media contents has becomean important issue in information security. Many researchers pay more attentions onimage hashing which is an initiative authentication method. Image hashing maps animage to a short binary sequence representing the image’s characteristics. In thisdissertation, we research on the basic theory of image hash and propose several imagehashing methods. The contributions of this dissertation are listed as follows:
1．Image hashing using shape feature
Shape feature of an object is invariant to color and environment. We propose animage hashing method based on Zernike moments. The amplitude and phase ofZernike moments of preprocessed image are quantified, connected and permuted togenerate the image hash. The hash length is216bits. It is robust against thecontent-preserving procession such as rotation. It has good performance ofdifferentiating similar images and forged images.
2．Image hashing based on conformal mapping and Zernike moments
The input image is pseudo-randomly partitioned into blocks, which are resized tobecome squares of a standard size and mapped to a unit circle by conformal mapping.Zernike moments of the circular-shaped “images” are calculated. All amplitudes andphases of the modified Zernike moments are concatenated to form an intermediatehash sequence. The final hash is produced by pseudo-randomly permuting theintermediate hash. This method extracts local information to form image hash, so ithas long hash length, good robustness and uniqueness. Image blocking allows the tampered regions in the image to be correctly located. Compared to some othertechniques, the method has low probabilities of collision and errors.
3．Image hashing for color forgery detection
The color space is transmitted from RGB to YCbCr. Then each component ismapped from rectangle to circle. At last the amplitude and phase of Zernikemoments is connected and permuted to form the final image hash. The hash length is66numbers, which is longer than the first method. But it includes the intensity andcolor information of the image, so this method can be used to detect unusual contentand color changes.
4．Image hashing based on saliency feature and Zernike moments
Many previous image hash schemes are either based on global or local featureswith various advantages and disadvantages. Both global and local features are used inproposing hash sequence. The global features are based on Zernike momentsrepresenting luminance and chrominance characteristics of the image as a whole. Thelocal features include position and texture information of salient regions in the image.Secret keys are introduced in feature extraction and hash construction for security.While being robust against content-preserving image processing, the hash is sensitiveto malicious tampering and therefore applicable to image authentication. Bydecomposing the hashes, the type of image forgery and location of forged areas can bedetermined.
At last these methods are compared in conclusions.
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