Benchmarking for Steganography

Pevný, Tomáš; Fridrich, Jessica

doi:10.1007/978-3-540-88961-8_18

Cited by 56 publications

(22 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We also observed that as we lowered the compression quality of JPEG images, there was a decrease on the number of pixels located in high complexity sub-domains. JPEG compression removes high frequency details from images as considered by Pevny and Fridrich [8]. Furthermore, the number of image artifacts increases as we lower the compression quality.…”

Section: Resultsmentioning

confidence: 99%

Entropy Quad-Trees for High Complexity Regions Detection

Vetro

Simovici

Ding

2013

Advances in Abstract Intelligence and Soft Computing

View full text Add to dashboard Cite

This paper introduces entropy quad-trees, which are structures derived from quad-trees by allowing nodes to split only when those correspond to sufficiently complex sub-domains of a data domain. Complexity is evaluated using an information-theoretic measure based on the analysis of the entropy associated to sets of objects designated by nodes. An alternative measure related to the concept of box-counting dimension is also explored. Experimental results demonstrate the efficiency of entropy quad-trees to mine complex regions. As an application, we used our proposed technique in the initial stage of a crater detection algorithm using digital images taken from Mars surface. Additional experimental results are provided that demonstrate the crater detection performance and analyze the effectiveness of entropy quad-trees for high-complexity regions detection in the pixel space with significant presence of noise. This work is focused on 2-dimensional image domains, but can be generalized to higher dimensional data. Keywords: Entropy, Box-Counting Dimension, Quad-trees, Circular Hough Transform Entropy Quad-Trees for High Complexity Regions DetectionThe concept of complexity relates to the presence of variation. In science there are many approaches that characterize complexity. A variety of scientific fields have dealt with complex mechanisms, simulations, systems, behavior and data complexity as those have always been a part of our environment. In this work, we focus on the topic of data complexity which is studied in information theory. While randomness is not considered complexity in certain areas such as those related to the study of complex systems, information theory tends to assign high values of complexity to random noise. Many fields benefit from the identification of content or noise related complex areas. In data hiding, adaptive steganography takes advantage of high concentration of self-information on high complexity areas originated from both content and noise to embed data. The authors of [1] describe the benefits of selective embedding related to the reduction of perceptual degradation for transform domain steganographic techniques. Bio diversity is another area where complexity can be used for identification and localization of different species. In this case, the complexity originated from content is more important than the one originated from noise.Our goal in this paper is to introduce a variant of quad-trees for mining high complexity sub-domains of a data domain. A quad-tree is a tree structure defined on a finite set of nodes that either contains no nodes or is comprised of a root node and 4 quad-subtrees. In a full quad-tree, each node is either a leaf or has degree exactly 4. Our variant of quad-trees requires that each node that has descendants corresponds to a region that has a sufficient level of diversity as assessed by the value of an information-theoretical measure. We also present an alternative measure that has its roots in fractal geometry where the so called box-counting dimens...

show abstract

Section: Resultsmentioning

confidence: 99%

Entropy Quad-Trees for High Complexity Regions Detection

Vetro

Simovici

Ding

2013

Advances in Abstract Intelligence and Soft Computing

View full text Add to dashboard Cite

show abstract

“…Then, the relative payload is fixed to 0.2bpp in order to reduce the complexity of the grid optimization method. The undetectability is evaluated by Maximum Mean Discrepancy [12]. The experimental results of the grid optimization method are shown in Table 1.…”

Section: Initialization Of Parametersmentioning

confidence: 99%

An Image Steganography Algorithm based on the Quantitative Features of Higher Order Local Model

Hao¹,

Zhou²

2016

IJSIA

View full text Add to dashboard Cite

show abstract

“…MMD has been used for benchmarking the security of steganography schemes, 14,15 but here it is simply a measure for comparing two actors' sets of feature vectors. It is ideal for our purposes because it can be estimated, even for very high dimensional vectors, from relatively few empirical samples: given n observations x = (x 1 , .…”

Section: Maximum Mean Discrepancy (Mmd)mentioning

confidence: 99%

A new paradigm for steganalysis via clustering

Ker

Pevný

2011

SPIE Proceedings

View full text Add to dashboard Cite

We propose a new paradigm for blind, universal, steganalysis in the case when multiple actors transmit multiple objects, with guilty actors including some stego objects in their transmissions. The method is based on clustering rather than classification, and it is the actors which are clustered rather than their individual transmitted objects. This removes the need for training a classifier, and the danger of training model mismatch. It effectively judges the behaviour of actors by assuming that most of them are innocent: after performing agglomerative hierarchical clustering, the guilty actor(s) are clustered separately from the innocent majority. A case study shows that this works in the case of JPEG images. Although it is less sensitive than steganalysis based on specifically-trained classifiers, it requires no training, no knowledge of the embedding algorithm, and attacks the pooled steganalysis problem.

show abstract

Benchmarking for Steganography

Cited by 56 publications

References 40 publications

Entropy Quad-Trees for High Complexity Regions Detection

Entropy Quad-Trees for High Complexity Regions Detection

An Image Steganography Algorithm based on the Quantitative Features of Higher Order Local Model

A new paradigm for steganalysis via clustering

Contact Info

Product

Resources

About