Statistical Decision Methods in Hidden Information Detection

Zitzmann, Cathel; Cogranne, Rémi; Retraint, Florent; Nikiforov, Igor; Fillatre, Lionel; Cornu, Philippe

doi:10.1007/978-3-642-24178-9_12

Cited by 22 publications

(22 citation statements)

References 11 publications

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“…Recently, the WS detector was rederived [26] using invariant hypothesis testing by adopting a parametric model for the cover. An Asymptotically Universally Most Powerful (AUMP) test that seems to coincide with a generalized likelihood ratio was derived in [7].…”

Section: Introductionmentioning

confidence: 99%

Steganalysis of LSB Replacement Using Parity-Aware Features

Fridrich

Kodovský

2013

Information Hiding

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Abstract. Detection of LSB replacement in digital images has received quite a bit of attention in the past ten years. In particular, structural detectors together with variants of Weighted Stego-image (WS) analysis have materialized as the most accurate. In this paper, we show that further surprisingly significant improvement is possible with machinelearning based detectors utilizing co-occurrences of neighboring noise residuals as features. Such features can leverage dependencies among adjacent residual samples in contrast to the WS detector, which implicitly assumes that the residuals are mutually independent. Further improvement is achieved by adapting the features for detection of LSB replacement by making them aware of pixel parity. To this end, we introduce two key novel concepts -calibration by parity and parity-aware residuals. It is shown that, at least for a known cover source when a binary classifier can be built, its accuracy is markedly better in comparison with the best structural and WS detectors in both uncompressed images and in decompressed JPEGs. This improvement is especially significant for very small change rates. A simple feature selection algorithm is used to obtain interesting insight that reveals potentially novel directions in structural steganalysis.

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Section: Introductionmentioning

confidence: 99%

Steganalysis of LSB Replacement Using Parity-Aware Features

Fridrich

Kodovský

2013

Information Hiding

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“…As recently shown [6,8], the WS implicitly assumes that the quantization step is negligible. Let us rewrite the LR test for JSteg detection based on a Gaussian distribution model of DCT coefficients.…”

Section: Comparison With Prior Artmentioning

confidence: 99%

“…Although the LSB-replacement steganalysis method (see [5][6][7][8][9][10]) has been studied for many years, it can be noted that most of the prior-art detectors are designed to detect data hidden in the spatial domain. In addition, for only a few detectors, the statistical properties have been studied and established, referred to as the optimal detectors.…”

Section: State Of the Artmentioning

confidence: 99%

“…Since the DCT coefficients can only take value into a discrete set, the distribution P θ may be represented by its probability mass function (pmf ) denoted P θ = {p θ [ u] }; for simplicity b , it is assumed in this paper that u ∈ Z. Let us denote Q R θ the probability distribution of usable DCT coefficients from the stego-image after embedding a message with payload R. A short calculation shows that, see [8,12,13], the stegoimage distribution may be represented by following the pmf…”

Section: Problem Statementmentioning

confidence: 99%

“…In practice, when the rate R is not known, one can try to design a test which is locally optimal around a given payload rate, named Locally Asymptotically Uniformly Most Powerful (LAUMP) test, as proposed in [6,8], but this lies outside the scope of this paper.…”

Section: Optimal Detection Frameworkmentioning

confidence: 99%

See 2 more Smart Citations

Steganalysis of JSteg algorithm using hypothesis testing theory

Qiao

Retraint

Cogranne

et al. 2015

EURASIP J. on Info. Security

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This paper investigates the statistical detection of JSteg steganography. The approach is based on a statistical model of discrete cosine transformation (DCT) coefficients challenging the usual assumption that among a subband all the coefficients are independent and identically distributed (i. i. d. ). The hidden information-detection problem is cast in the framework of hypothesis testing theory. In an ideal context where all model parameters are perfectly known, the likelihood ratio test (LRT) is presented, and its performances are theoretically established. The statistical performance of LRT serves as an upper bound for the detection power. For a practical use where the distribution parameters are unknown, by exploring a DCT channel selection, a detector based on estimation of those parameters is designed. The loss of power of the proposed detector compared with the optimal LRT is small, which shows the relevance of the proposed approach.

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Statistical Detection of LSB Matching Using Hypothesis Testing Theory

et al. 2013

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This paper investigates the detection of information hidden by the Least Significant Bit (LSB) matching scheme. In a theoretical context of known image media parameters, two important results are presented. First, the use of hypothesis testing theory allows us to design the Most Powerful (MP) test. Second, a study of the MP test gives us the opportunity to analytically calculate its statistical performance in order to warrant a given probability of false-alarm. In practice when detecting LSB matching, the unknown image parameters have to be estimated. Based on the local estimator used in the Weighted Stego-image (WS) detector, a practical test is presented. A numerical comparison with state-of-the-art detectors shows the good performance of the proposed tests and highlights the relevance of the proposed methodology.

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Statistical Decision Methods in Hidden Information Detection

Cited by 22 publications

References 11 publications

Steganalysis of LSB Replacement Using Parity-Aware Features

Steganalysis of LSB Replacement Using Parity-Aware Features

Steganalysis of JSteg algorithm using hypothesis testing theory

Statistical Detection of LSB Matching Using Hypothesis Testing Theory

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