Image Steganalysis Based on Moments of Characteristic Functions Using Wavelet Decomposition, Prediction-Error Image, and Neural Network

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

doi:10.1109/icme.2005.1521412

Cited by 118 publications

(88 citation statements)

References 10 publications

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“…The next verification of proposed steganalytic method was focused on its comparison with existing image steganalytic methods. Proposed method used an Ensemble classifier and statistical vector with the length SS (285+46) was compared with current image steganalytic methods published in [5] and [7].…”

Section: Resultsmentioning

confidence: 99%

“…The classification into stego or cover object class was performed by CNPCA classifier [6]. The second compared method [7] uses a DWT domain for the extraction of statistical parameters. As parameters were used statistical moments of the characteristic functions in the verified images.…”

Section: Resultsmentioning

confidence: 99%

“…Third level of 2D Haar discrete wavelet transformation (DWT) [7] is used for the extraction of statistical features in the proposed steganalytic method. V i (x, y), H i (x, y) and D i (x, y) represent detail coefficients of image in vertical, diagonal and horizontal direction while A i (x, y) expresses approximation coefficients of specific level.…”

Section: Statistical Featuresmentioning

confidence: 99%

“…The classification into stego or cover object class was performed by CNPCA method (Class-wise Non-Principal Component Analysis) [6]. The second technique [7] utilizes DWT domain for the parameters extraction. As the parameters were chosen statistical moments of testing image characteristic functions also calculated for the predictionversion image between testing image and its predictable version.…”

Section: Introductionmentioning

confidence: 99%

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Universal statistical steganalytic method

Broda

Hajduk

Levický

2017

Journal of Electrical Engineering

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Novel image steganalytic method used to detection of secret message in static images is introduced in this paper. This method is based on statistical steganalysis (SS), where statistical vector is composed by 285 statistical features (parameters) extracted from DCT (Discrete Cosine Transformation) domain and 46 features extracted mainly from DWT (Discrete Wavelet Transformation) domain. Classification process was realized by Ensemble classifier that was helpful in reduction of computational and time complexity. Proposed steganalytic method was verified by detection of popular image steganographic methods. Novel method was also compared with existing steganalytic methods by overall detection accuracy of a secret message.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Statistical Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Universal statistical steganalytic method

Broda

Hajduk

Levický

2017

Journal of Electrical Engineering

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“…In [2,3], Xuan et al and Shi et al presented another universal steganalysis framework, using the moments of characteristic functions of the given test image, its prediction-error image, and all of their wavelet transform subbands as features, denoted by MC, providing a better performance than [1] in general.…”

mentioning

confidence: 99%

Effect of Recompression on Attacking JPEG Steganographic Schemes An Experimental Study

Shi

Chen

et al. 2007

2007 IEEE International Symposium on Circuits and Systems (ISCAS)

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Abstract-In the implementation of a few JPEG steganographic schemes such as OutGuess and F5, an additional JPEG compression may take place before data embedding. The effect of this recompression on the performances of steganalyzers is experimentally studied and reported in this paper. Through a group of carefully designed experimental works, we show that the training and testing procedures adopted in classification are of great importance. An improper training and testing procedure may lead to poor steganalysis performance even for a powerful steganalyzer or an accurate performance comparison. Some other informative observations are presented in the paper as well.

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Detection of Image Splicing Based on Hilbert-Huang Transform and Moments of Characteristic Functions with Wavelet Decomposition

Shi

2006

Digital Watermarking

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Image Steganalysis Based on Moments of Characteristic Functions Using Wavelet Decomposition, Prediction-Error Image, and Neural Network

Abstract: Abstract

Cited by 118 publications

References 10 publications

Universal statistical steganalytic method

Universal statistical steganalytic method

Effect of Recompression on Attacking JPEG Steganographic Schemes An Experimental Study

Detection of Image Splicing Based on Hilbert-Huang Transform and Moments of Characteristic Functions with Wavelet Decomposition

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