Steganalysis Based on Multiple Features Formed by Statistical Moments of Wavelet Characteristic Functions

Xuan, Guixin; Shi, Yun Q.; Gao, Jianjiong; Zou, Dekun; Yang, Chengyun; Zhu, Zhenping; Chai, Peiqi; Chen, Chunhua; Chen, Wen

doi:10.1007/11558859_20

Cited by 126 publications

(101 citation statements)

References 10 publications

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“…5. Xuan-39: Spatial domain steganalysis proposed by Xuan et al [13] (a 39-dimensional feature vector). 6.…”

Section: Detection Resultsmentioning

confidence: 99%

“…It is known that, the higher order statistics, in general, are difficult to match, model, or restore. Recently, blind steganalysis algorithms [9][10][11][12][13][14][15][16] have been proposed that employ supervised learning to distinguish between the plain cover and stego images, and also identify the particular hiding algorithm used for steganography. These techniques bank on the fact that there are some image features that are modified during the embedding process which can be used as an input to the learning machine.…”

Section: Introductionmentioning

confidence: 99%

“…We evaluate the method against several recent blind steganalysis schemes, which include Farid's 72-dimensional wavelet-based features [19], Pevny and Fridrich's 23-dimensional DCT-based features [9] and 274-dimensional merged Markov and DCT features [10], a feature vector comprising of histogram of DCT coefficients, Xuan et al's statistical moments based spatial domain steganalysis features [13], and Chen et al's 324-dimensional JPEG steganalysis feature [20]. We find that the presented method, YASS, is completely undetectable for most of the embedding configurations, while the competing algorithms, OutGuess and StegHide, are detectable at the same hiding rates.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…5. Xuan-39: Spatial domain steganalysis proposed by Xuan et al [13] (a 39-dimensional feature vector). 6.…”

Section: Detection Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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YASS: Yet Another Steganographic Scheme That Resists Blind Steganalysis

2007

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“…Such a phenomenon usually do not appear in natural images, therefore its appearance would attract the steganalyst's attention. This is why histogram and histogram characteristic functions have been extensively used in measuring the security of stegonagraphic schemes [18][19][20]. By analysing the histogram of the stego-image, the attacker can easily detect the spheres in the form of aggregated clusters separated by large gaps in the histogram.…”

Section: Histogram Analysis and ε-Securementioning

confidence: 99%

Progressive Exponential Clustering-Based Steganography

2010

EURASIP J. Adv. Signal Process.

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Cluster indexing-based steganography is an important branch of data-hiding techniques. Such schemes normally achieve good balance between high embedding capacity and low embedding distortion. However, most cluster indexing-based steganographic schemes utilise less efficient clustering algorithms for embedding data, which causes redundancy and leaves room for increasing the embedding capacity further. In this paper, a new clustering algorithm, called progressive exponential clustering (PEC), is applied to increase the embedding capacity by avoiding redundancy. Meanwhile, a cluster expansion algorithm is also developed in order to further increase the capacity without sacrificing imperceptibility.

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“…There are two major classes of steganalytic methods-targeted attacks and blind steganalysis. Targeted attacks use the knowledge of the embedding algorithm [11], while blind approaches are not tailored to any specific embedding paradigm [9,8,4,5,21,28,15]. Blind approaches can be thought of as practical embodiments of Cachin's [6] definition of steganographic security.…”

Section: Introductionmentioning

confidence: 99%

Determining the stego algorithm for JPEG images

Pevný

Fridrich

2006

IEE Proc. Inf. Secur.

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The goal of Forensic Steganalysis is to detect the presence of embedded data and eventually extract the secret message. A necessary step toward extracting the data is determining the steganographic algorithm used to embed the data. In this paper, we construct blind classifiers capable of detecting steganography in JPEG images and assigning stego images to 6 popular JPEG embedding algorithms. The classifiers are support vector machines that use 23 calibrated DCT feature calculated from the luminance component.

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Steganalysis Based on Multiple Features Formed by Statistical Moments of Wavelet Characteristic Functions

Cited by 126 publications

References 10 publications

YASS: Yet Another Steganographic Scheme That Resists Blind Steganalysis

YASS: Yet Another Steganographic Scheme That Resists Blind Steganalysis

Progressive Exponential Clustering-Based Steganography

Determining the stego algorithm for JPEG images

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