Channel Steganalysis

Steinebach, Martin; Ester, Andre; Liu, Huajian

doi:10.1145/3230833.3233266

Cited by 7 publications

(6 citation statements)

References 3 publications

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“…Current steganography countermeasures are usually tailored for testbed environments, where they provide sufficient results, see [4] for a comprehensive overview. However, not only do real-world applications demand very low false-positive rates as false-positives accumulate to large numbers for large-scale scenarios [31], they also have to deal with continuously changing data. For instance, since the invention of the ARPANET, the Internet's traffic characteristics continuously kept changing [3].…”

Section: Anticipated Steganography Developments In the Context Of Patternsmentioning

confidence: 99%

A Revised Taxonomy of Steganography Embedding Patterns

Wendzel¹,

Caviglione

Mazurczyk

et al. 2021

Proceedings of the 16th International Conference on Availability, Reliability and Security

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Steganography embraces several hiding techniques which spawn across multiple domains. However, the related terminology is not unified among the different domains, such as digital media steganography, text steganography, cyber-physical systems steganography, network steganography (network covert channels), local covert channels, and out-of-band covert channels. To cope with this, a prime attempt has been done in 2015, with the introduction of the so-called hiding patterns, which allow to describe hiding techniques in a more abstract manner. Despite significant enhancements, the main limitation of such a taxonomy is that it only considers the case of network steganography.Therefore, this paper reviews both the terminology and the taxonomy of hiding patterns as to make them more general. Specifically, hiding patterns are split into those that describe the embedding and the representation of hidden data within the cover object.This work is licensed under a Creative Commons Attribution International 4.0 License.

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Section: Anticipated Steganography Developments In the Context Of Patternsmentioning

confidence: 99%

A Revised Taxonomy of Steganography Embedding Patterns

Wendzel¹,

Caviglione

Mazurczyk

et al. 2021

Proceedings of the 16th International Conference on Availability, Reliability and Security

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“…In contrast to [18] where it is assumed that multiple images are used for a steganografic communication channel, we only consider cases in which individual images are used as analysis material. To date, however, there are no research studies describing cases in which several versions of the same image (reference images) exist.…”

Section: Research Goalmentioning

confidence: 99%

Cover-aware Steganalysis

Bunzel

Steinebach

Liu

2021

JCSANDM

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The increasing digitization offers new ways, possibilities and needs for a secure transmission of information. Steganography and its analysis constitute an essential part of IT-Security. In this work we show how methods of blind-steganalysis can be improved to work in cover-aware scenarios, we will call this non-blind steganalysis. The main objective was to examine how to take advantage of the knowledge of reference images to maximize the accuracy-rate of the analysis. Therefore we evaluated common stego-tools and their embedding algorithms and established a dataset of 353110 images. The images have been applied to test the potency of the improved methods of the non-blind steganalysis. The results show that the accuray can be significantly improved by using cover-images to produce reference images. Also the aggregation of the outcomes has shown to have a positive impact on the accuracy. Particularly noteworthy is the correlation between the qualities of the stego- and coverimages. Only by consindering both, the accuracy could strongly be improved. Interestingly the difference between both qualities also has a deep impact on the results.

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“…Current steganography countermeasures are usually tailored for testbed environments, where they provide sufficient results, see [4] for a comprehensive overview. However, not only do real-world applications demand very low falsepositive rates as false-positives accumulate to large numbers for large-scale scenarios [31], they also have to deal with continuously changing data. For instance, since the invention of the ARPANET, the Internet's traffic characteristics continuously kept changing [3].…”

Section: Artificial Retransmissions Modulationmentioning

confidence: 99%

A Revised Taxonomy of Steganography Embedding Patterns

Wendzel,

Caviglione,

Mazurczyk

et al. 2021

Preprint

View full text Add to dashboard Cite

Steganography embraces several hiding techniques which spawn across multiple domains. However, the related terminology is not unified among the different domains, such as digital media steganography, text steganography, cyber-physical systems steganography, network steganography (network covert channels), local covert channels, and out-of-band covert channels. To cope with this, a prime attempt has been done in 2015, with the introduction of the socalled hiding patterns, which allow to describe hiding techniques in a more abstract manner. Despite significant enhancements, the main limitation of such a taxonomy is that it only considers the case of network steganography.Therefore, this paper reviews both the terminology and the taxonomy of hiding patterns as to make them more general. Specifically, hiding patterns are split into those that describe the embedding and the representation of hidden data within the cover object.As a first research action, we focus on embedding hiding patterns and we show how they can be applied to multiple domains of steganography instead of be-ing limited to the network scenario. Additionally, we exemplify representation patterns using network steganography. Our pattern collection is available under https://patterns.ztt.hs-worms.de.

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Channel Steganalysis

Cited by 7 publications

References 3 publications

A Revised Taxonomy of Steganography Embedding Patterns

A Revised Taxonomy of Steganography Embedding Patterns

Cover-aware Steganalysis

A Revised Taxonomy of Steganography Embedding Patterns

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