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首页> 外文学位 >Compression aided feature based steganalysis of perturbed quantization steganography in JPEG images.
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Compression aided feature based steganalysis of perturbed quantization steganography in JPEG images.

机译：基于压缩辅助特征的JPEG图像中扰动量化隐写术的隐写分析。

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摘要

Steganography is the process of embedding data within a larger cover object. This larger cover object is usually a media file such as an image, audio or video file. The goal of the process is to embed the data in such a way that it is impossible to differentiate an ordinary object from a cover object. With the proliferation of media on the Internet, steganography has become relatively easy for two reasons. First, passing media between Internet users directly or posting the media on a website is a common practice and arouses no suspicion. Second, broadband has made using larger cover media possible thereby making changes to media via embedding harder to detect.; Steganalysis is the process of trying to determine if an object contains embedded data. There are two main methods used to detect steganography. The first and most commonly used method is to exploit the statistics of the image. The second method is to recompress the media and compare the results to known performance ranges for the compression algorithm.; This thesis uses both compression and traditional statistical methods to analyze existing steganographic techniques. There will be a focus on newer steganographic techniques, which operate in the transformation domain but attention will also be given to older spatial domain steganography techniques. Most new steganographic techniques embed messages into the cover media by degrading the quality of the cover media in a controlled way. The resulting degraded cover media is perceptually identical to the original media but a potential adversary is deprived of having access to an image of the same quality as the original cover image. The result is a more secure steganographic system that introduces less distortion than earlier methods.; A blind steganalysis algorithm that analyzes both the statistical properties of the embedding domain and the compressibility of the image can be compressed is proposed. A non-linear support vector machine will perform the classification. The input feature vector is composed primarily of statistical measures with a small but very influential compression component. A portion of the statistical features are created by finding the first, second, third and fourth order statistical moments of the transformation domain histograms. The remainder of the statistical portion is composed of correlation measures between carefully selected transformation domain coefficients. Higher order moments have higher miss rates than lower order moments because they more influenced by noise; however, research has shown that if appropriately treated, including higher order moments increases classifier accuracy. Using additional higher order moments as a basis for classification has the effect of increasing the size of the feature vector making accurate training difficult. To address this, classification is performed using each statistical moment order separately and a logical combination of the individual classifications used to produce a single classification for the presented image.; Previous work in steganalysis has shown that statistical analysis using support vector machines will provide good classification accuracy. The classification algorithm proposed in this thesis improves on these results by including more features than previous methods and weighting their influence appropriately. In addition, the inclusion of a compression feature vector improves hit rates by two to three percent and reduces false alarm rates similarly.
机译：隐写术是将数据嵌入更大的封面对象中的过程。较大的封面对象通常是媒体文件，例如图像，音频或视频文件。该过程的目标是以无法区分普通对象和掩盖对象的方式嵌入数据。随着Internet上媒体的普及，隐写术变得相对容易，原因有两个。首先，直接在Internet用户之间传递媒体或将媒体发布到网站上是一种常见做法，不会引起任何怀疑。其次，宽带使得使用更大的覆盖介质成为可能，从而使得通过嵌入变得难以检测到介质的变化。隐写分析是尝试确定对象是否包含嵌入式数据的过程。有两种主要方法可用于检测隐写术。第一种也是最常用的方法是利用图像的统计信息。第二种方法是重新压缩媒体，并将结果与​​压缩算法的已知性能范围进行比较。本文使用压缩和传统统计方法来分析现有的隐写技术。将关注于在转换域中运行的较新的隐写术技术，但是还将关注较旧的空间域隐写术技术。大多数新的隐写术技术通过以受控方式降低封面媒体的质量，将消息嵌入封面媒体中。最终导致降级的封面媒体在感觉上与原始媒体相同，但是潜在的对手被剥夺了访问与原始封面图像质量相同的图像的权限。结果是一种更安全的隐写系统，与早期方法相比，它引入的失真更少。提出了一种盲隐写分析算法，既分析了嵌入域的统计特性，又分析了图像的可压缩性。非线性支持向量机将执行分类。输入特征向量主要由统计量组成，其压缩量很小但影响很大。通过查找变换域直方图的第一，第二，第三和第四阶统计矩来创建一部分统计特征。统计部分的其余部分由精心选择的转换域系数之间的相关度量组成。高阶矩比低阶矩有更高的遗漏率，因为它们受噪声的影响更大。但是，研究表明，如果进行适当处理，则包括较高阶矩在内的所有内容都可以提高分类器的准确性。使用额外的高阶矩作为分类的基础具有增加特征向量大小的效果，从而难以进行精确训练。为了解决这个问题，分别使用每个统计矩顺序和用于生成所呈现图像的单个分类的各个分类的逻辑组合来执行分类。隐写分析的先前工作表明，使用支持向量机进行统计分析将提供良好的分类准确性。本文提出的分类算法通过包含比以前的方法更多的特征并适当地加权其影响来改进这些结果。另外，包含压缩特征向量可以将命中率提高百分之二到三，并类似地减少误报率。

著录项

  • 作者

    Thorpe, Christopher.;

  • 作者单位

    University of Delaware.$bDepartment of Electrical and Computer Engineering.;

  • 授予单位 University of Delaware.$bDepartment of Electrical and Computer Engineering.;
  • 学科 Engineering Electronics and Electrical.
  • 学位 M.S.
  • 年度 2007
  • 页码 114 p.
  • 总页数 114
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;
  • 关键词

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