Detection of Hiding in the Least Significant Bit

Dabeer, Onkar; Sullivan, K.; Madhow, Upamanyu; Chandrasekaran, S.; Manjunath, B.S.

doi:10.1109/tsp.2004.833869

Cited by 73 publications

(44 citation statements)

References 21 publications

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“…Further, good performance for composite hypotheses is given by the generalized likelihood ratio (GLR) test and for parameter estimators by the method of maximum likelihood (although the optimality of these extensions is not universal). One of the earliest uses of ML in steganalysis is found in [8], which derives the effect of LSB replacement on the probability mass function (PMF) of a signal; if the PMF of the cover source is known, it is possible to create a GLR test for the presence of data hidden by LSB overwriting and a ML estimate for the size of payload.…”

Section: Steganalysis and Structural Steganalysismentioning

confidence: 99%

“…In practice the PMF of the cover is not known: it must be estimated by either filtering the observed PMF [8] or postulating an "ideal" cover PMF ( [9] uses this latter approach, in a transform domain). The detectors are weak for three reasons: estimation of the cover PMF is subject to inaccuracy; considering only the PMF discards Markovicity in the cover source; and the detectors are unable to exploit the aforementioned structure of LSB replacement.…”

Section: Steganalysis and Structural Steganalysismentioning

confidence: 99%

“…But their exposition can become complex, and the methods themselves are often based on dubious statistical principles (for example, assuming that all observations of random variables equal the expectation). Statistical rigour is at the heart of maximum likelihood detectors [8][9][10][11] but their performance is weak. The blind classifier-based methods can detect a range of embedding methods (not limited to bit replacement) but have neither good detection power nor statistical rigour and we shall not consider this last class here.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Fusion of Maximum Likelihood and Structural Steganalysis

Ker

2007

Information Hiding

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Abstract. This paper draws together two methodologies for the detection of bit replacement steganography: the principle of maximum likelihood, which is statistically well-founded but has lead to weak detectors in practice, and so-called structural detection, which is sensitive but lacks optimality and can suffer from complicated exposition. The key novelty is to extend structural analysis to include a hypothetical "pre-cover", from which the cover object is imagined to derive. Here, maximum likelihood detection is presented for three structural detectors. Although the algebraic derivation is long, and maximizing the likelihood function difficult in practice, conceptually the new detectors are reasonably simple. Experiments show that the new detectors are the best performers yet, very significantly so in the detection of replacement of multiple bit planes.

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Section: Steganalysis and Structural Steganalysismentioning

confidence: 99%

Section: Steganalysis and Structural Steganalysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Fusion of Maximum Likelihood and Structural Steganalysis

Ker

2007

Information Hiding

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“…In spite of the absence of good universal models, recent steganalysis algorithms have been very successful by using a self-calibration method to approximate the statistics of the original cover (see, for example, Pevny and Fridrich [9,10], and Dabeer et al [17]). The calibration method typically used for JPEG steganography is quite simple; a few pixel rows and/or columns are cropped from the image so as to desynchronize it from the original JPEG grid and the resulting image is compressed again, which forms a good approximation of the cover image.…”

Section: Introductionmentioning

confidence: 99%

YASS: Yet Another Steganographic Scheme That Resists Blind Steganalysis

2007

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Abstract.A new, simple, approach for active steganography is proposed in this paper that can successfully resist recent blind steganalysis methods, in addition to surviving distortion constrained attacks. We present Yet Another Steganographic Scheme (YASS), a method based on embedding data in randomized locations so as to disable the selfcalibration process (such as, by cropping a few pixel rows and/or columns to estimate the cover image features) popularly used by blind steganalysis schemes. The errors induced in the embedded data due to the fact that the stego signal must be advertised in a specific format such as JPEG, are dealt with by the use of erasure and error correcting codes. For the presented JPEG steganograhic scheme, it is shown that the detection rates of recent blind steganalysis schemes are close to random guessing, thus confirming the practical applicability of the proposed technique. We also note that the presented steganography framework, of hiding in randomized locations and using a coding framework to deal with errors, is quite simple yet very generalizable.

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“…Since there are similarities between images and audios, in this section, we review some image steganalysis algorithms which may be helpful for the audio steganalysis. It should be noted that many steganalysis techniques are specific to some particular data hiding methods [2][11] [12][13] [14]. However, since the data-embedding method is typically unknown prior to detection, we focus on the design of a unified steganalysis algorithm to detect the presence of steganography independent of the steganography algorithms used.…”

Section: Related Workmentioning

confidence: 99%

A Novel Audio Steganalysis Based on High-Order Statistics of a Distortion Measure with Hausdorff Distance

Liu

Chiang

Corbett

et al.

Lecture Notes in Computer Science

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Abstract. Steganography can be used to hide information in audio media both for the purposes of digital watermarking and establishing covert communication channels. Digital audio provides a suitable cover for highthroughput steganography as a result of its transient and unpredictable characteristics. Distortion measure plays an important role in audio steganalysis -the analysis and classification method of determining if an audio medium is carrying hidden information. In this paper, we propose a novel distortion metric based on Hausdorff distance. Given an audio object x which could potentially be a stego-audio object, we consider its de-noised version x as an estimate of the cover-object. We then use Hausdorff distance to measure the distortion from x to x . The distortion measurement is obtained at various wavelet decomposition levels from which we derive high-order statistics as features for a classifier to determine the presence of hidden information in an audio signal. Extensive experimental results for the Least Significant Bit (LSB) substitution based steganography tool show that the proposed algorithm has a strong discriminatory ability and the performance is significantly superior to existing methods. The proposed approach can be easily applied to other steganography tools and algorithms.

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Detection of Hiding in the Least Significant Bit

Cited by 73 publications

References 21 publications

A Fusion of Maximum Likelihood and Structural Steganalysis

A Fusion of Maximum Likelihood and Structural Steganalysis

YASS: Yet Another Steganographic Scheme That Resists Blind Steganalysis

A Novel Audio Steganalysis Based on High-Order Statistics of a Distortion Measure with Hausdorff Distance

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