Download e-book for kindle: Advanced Statistical Steganalysis by Rainer Böhme

By Rainer Böhme

ISBN-10: 3642143121

ISBN-13: 9783642143120

ISBN-10: 364214313X

ISBN-13: 9783642143137

Steganography is the paintings and technology of hiding info in inconspicuous disguise info in order that even the lifestyles of a mystery message is saved private, and steganalysis is the duty of detecting mystery messages in covers. This learn monograph specializes in the function of canopy signs, the distinguishing function that calls for us to regard steganography and steganalysis in a different way from different secrecy innovations. the most theoretical contribution of the publication is an offer to constitution methods to provably safe steganography in response to their implied assumptions at the limits of the adversary and at the nature of covers. one other contribution is the emphasis on facing heterogeneity in hide distributions, the most important for safeguard analyses. The author's paintings enhances prior ways in line with details, complexity, chance and sign processing concept, and he provides quite a few useful implications. The medical advances are supported via a survey of the classical steganography literature; a brand new suggestion for a unified terminology and notation that's maintained in the course of the ebook; a serious dialogue of the consequences completed and their barriers; and an review of the opportunity of moving parts of this research's empirical standpoint to different domain names in info defense. The booklet is acceptable for researchers operating in cryptography and data protection, practitioners within the company and nationwide safeguard domain names, and graduate scholars focusing on multimedia safeguard and information hiding.

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36 2 Principles of Modern Steganography and Steganalysis Standard JPEG compression cuts a greyscale image into blocks of 8 × 8 pixels, which are separately transformed into the frequency domain by a 2D DCT. The resulting 64 DCT coefficients are divided by subband-specific quantisation factors, calculated from a JPEG quality parameter q, and then rounded to the closest integer. In the notation of Sect. 3, the quantised DCT coefficients y ∗ can be obtained as follows: y ∗ = q y + 1/2 (Quant(q, i))−1 for i = j 0 otherwise.

Moreover, these multivariate dependencies are usually non-stationary and thus hard to describe with statistical models. , preserving the dependence structure) is not trivial. Another problem arises from file format conventions. From an informationtheoretic point of view, interdependencies between samples are seen as a redundancy, which consumes excess storage and transmission resources. Therefore, common file formats employ lossy source coding to achieve leaner representations of media data. Steganography which is not robust to lossy coding would only be possible in uncompressed or losslessly compressed file formats.

This means that this symbol is used to convey the secret message and can be interpreted by Extract. In (0) (1) fact, xi = xi if the steganographic meaning of the cover symbol already matches the respective part of the message. Note that there is not necessarily a one-to-one relation between message symbols and cover symbols carrying (1) secret message information xi , as groups of cover symbols can be interpreted jointly in certain stego systems (cf. Sect. 2). Without loss of generality, we make the following assumptions in this book: • The secret message m ∈ M = {0, 1}∗ is a vector of bits with maximum entropy.

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Advanced Statistical Steganalysis by Rainer Böhme

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