Steganalysis Based on Difference Image

Sun, Yi; Liu, Fenlin; Liu, Bin; Wang, Ping

doi:10.1007/978-3-642-04438-0_16

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…We consider the simplest residual image: the difference image in horizontal direction. Here we remark that the difference image has already been widely adopted in steganalysis [10,11]. For a given image I, its horizontal difference image is defined as I h = I * f h , where f h is the horizontal convolution kernel (1, −1).…”

Section: The Proposed Methodsmentioning

confidence: 99%

Identifying computer generated graphics VIA histogram features

Yang

2011

2011 18th IEEE International Conference on Image Processing

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Discriminating computer generated graphics from photographic images is a challenging problem of digital forensics. An important approach to this issue is to explore usual image statistics. In this way, when the statistical distributions (i.e., histograms) of some types of residual images are established, previous works usually apply operations on these histograms or compute statistical quantities to extract features. However, as the histograms are fundamental resources and can present most image information, the histograms themselves can be directly used as features and we do not need further manipulations on them. Based on this consideration, we simply take several highest histogram bins of the difference images as features to carry out classification, and these simple histogram features work well in terms of both detection accuracy and computational complexity. Actually, experimental results demonstrate that, with only 112 features, the proposed method outperforms some state-of-the-art works.

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Section: The Proposed Methodsmentioning

confidence: 99%

Identifying computer generated graphics VIA histogram features

Yang

2011

2011 18th IEEE International Conference on Image Processing

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“…In the experiments, the first steganalytic detector [2] adopts the Difference Characteristic Function Moments as the features, and Fisher linear discriminator (FLD) as the classifier. The detection performance for the existing LSB matching is excellent [2].…”

Section: A Roc Curves Metricmentioning

confidence: 99%

“…These dependences are violated by LSB matching. Many steganalysis methods utilized this fact [1] [2]. The LSB matching also needs to be improved.…”

Section: Introductionmentioning

confidence: 99%

Optimized LSB Matching Steganography Based on Fisher Information

Sun¹,

Niu

Tang³

et al. 2012

JMM

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This paper proposes an optimized LSB matching steganography based on Fisher Information. The embedding algorithm is designed to solve the optimization problem, in which Fisher information is the objective function and embedding transferring probabilities are variables to be optimized. Fisher information is the quadratic function of the embedding transferring probabilities, and the coefficients of quadratic term are determined by the joint probability distribution of cover elements. By modeling the groups of elements in a cover image as Gaussian mixture model, the joint probability distribution of cover elements for each cover image is obtained by estimating the parameters of Gaussian mixture distribution. For each sub-Gaussian distribution in Gaussian mixture distribution, the quadratic term coefficients of Fisher information are calculated, and the optimized embedding transferring probabilities are solved by quadratic programming. By maximum posteriori probability principle, cover pixels are classified as the categories corresponding to sub-Gaussian distributions. At last, in order to embed message bits, pixels chose to add or subtract one according to the optimized transferring probabilities of the category. The experiments show that the security performance of this new algorithm is better than the existing LSB matching.

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Calibration Based Reliable Detector for Detecting LSB Matching Steganography

Peng

Yang

2013

Digital Forensics and Watermaking

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Steganalysis Based on Difference Image

Cited by 5 publications

References 21 publications

Identifying computer generated graphics VIA histogram features

Identifying computer generated graphics VIA histogram features

Optimized LSB Matching Steganography Based on Fisher Information

Calibration Based Reliable Detector for Detecting LSB Matching Steganography

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