Proceedings ArticleDOI
Image steganalysis with binary similarity measures
Ismail Avcibas,Nasir Memon,Bulent Sankur +2 more
- Vol. 3, pp 645-648
TLDR
A novel technique for steganalysis of images that have been subjected to embedding by steganographic algorithms that is found to have complementary performance vis-à-vis Farid's scheme in that they outperform each other in alternate embedding techniques.Abstract:
We present a novel technique for steganalysis of images that have been subjected to least significant bit (LSB) type steganographic algorithms. The seventh and eighth bit planes in an image are used for the computation of several binary similarity measures. The basic idea is that the correlation between the bit planes as well the binary texture characteristics within the bit planes differs between a stego-image and a cover-image. These telltale marks can be used to construct a steganalyzer, that is, a multivariate regression scheme to detect the presence of a steganographic message in an image.read more
Citations
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Book
Digital Watermarking
TL;DR: Digital Watermarking covers the crucial research findings in the field and explains the principles underlying digital watermarking technologies, describes the requirements that have given rise to them, and discusses the diverse ends to which these technologies are being applied.
Journal ArticleDOI
Review: Digital image steganography: Survey and analysis of current methods
TL;DR: This paper provides a state-of-the-art review and analysis of the different existing methods of steganography along with some common standards and guidelines drawn from the literature and some recommendations and advocates for the object-oriented embedding mechanism.
Journal ArticleDOI
Rich Models for Steganalysis of Digital Images
Jessica Fridrich,Jan Kodovsky +1 more
TL;DR: A novel general strategy for building steganography detectors for digital images by assembling a rich model of the noise component as a union of many diverse submodels formed by joint distributions of neighboring samples from quantized image noise residuals obtained using linear and nonlinear high-pass filters.
Journal ArticleDOI
Ensemble Classifiers for Steganalysis of Digital Media
TL;DR: This paper proposes an alternative and well-known machine learning tool-ensemble classifiers implemented as random forests-and argues that they are ideally suited for steganalysis.
Book
Steganography in Digital Media: Principles, Algorithms, and Applications
TL;DR: This clear, self-contained guide shows you how to understand the building blocks of covert communication in digital media files and how to apply the techniques in practice, including those of steganalysis, the detection of Steganography.
References
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TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
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The Elements of Statistical Learning
TL;DR: Chapter 11 includes more case studies in other areas, ranging from manufacturing to marketing research, and a detailed comparison with other diagnostic tools, such as logistic regression and tree-based methods.
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A comparative study of texture measures with classification based on featured distributions
TL;DR: This paper evaluates the performance both of some texture measures which have been successfully used in various applications and of some new promising approaches proposed recently.