Rich model for Steganalysis of color images

Goljan, Miroslav; Fridrich, Jessica; Cogranne, Rémi

doi:10.1109/wifs.2014.7084325

Cited by 126 publications

(111 citation statements)

References 19 publications

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“…We illustrate these results in Tables 1 and 2. S-UNIWARD and WOW methods were tested with different relative payloads {0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5} Bits Per channel (BP C) against the method of color rich model [4].…”

Section: Resultsmentioning

confidence: 99%

“…The first set, produced by [4], gives 18157 features. The second set gives 3000 features obtained from the correlation of different R, G, B channel gradients.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…From two different components, Goljan et al [4] developed a color image model which is called the Spatio-Color Rich Model (SCRM Q1, where 'C' represents the color version of the Spatial Rich Model -SRM Q1- [12], with 'Q1' representing fixed quantization q = 1 ) produced from two different components. The first component is the SRM Q1 with a single quantization step q = 1 and dimensionality of 12753 features.…”

Section: Literature Reviewmentioning

confidence: 99%

“…We used the spatial color rich model for color image steganalysis in [4] which produced 18157 features. This method extracts the noise residual from each color channel separately by the following formula:…”

Section: Color Rich Modelmentioning

confidence: 99%

“…In this paper, we propose new features to enrich the color rich model [4], which is formed by co-occurrences of residuals taken across the color channels. Section 2 is dedicated to methods related to color image steganalysis.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Color Image Stegananalysis Using Correlations between RGB Channels

Abdulrahman

Chaumont²,

Montesinos³

et al. 2015

2015 10th International Conference on Availability, Reliability and Security

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Section: Resultsmentioning

confidence: 99%

“…The first set, produced by [4], gives 18157 features. The second set gives 3000 features obtained from the correlation of different R, G, B channel gradients.…”

Section: Proposed Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Color Rich Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Color Image Stegananalysis Using Correlations between RGB Channels

Abdulrahman

Chaumont²,

Montesinos³

et al. 2015

2015 10th International Conference on Availability, Reliability and Security

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Color images steganalysis using rgb channel geometric transformation measures

Abdulrahman

Chaumont

Montesinos

et al. 2016

Security Comm Networks

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In recent years, information security has received a great deal of attention. To give an example, steganography techniques are used to communicate in a secret and invisible way. Digital color images have become a good medium for digital steganography because of their easy manipulation as carriers via Internet, e‐mails, or used on websites. The main goal of steganalysis is to detect the presence of hidden messages in a digital media. The proposed method is a further extension of the authors' previous work: steganalysis based on color feature correlation and machine learning classification. Fusing features with those obtained from color‐rich models allows increasing the detectability of hidden messages in the color images. Our new proposition uses two types of features, computed between color image channels. The first type of feature reflects local Euclidean transformations, and the second one reflects mirror transformations. These geometric measures are obtained by the sine and cosine of gradient angles between all the color channels. Features are extracted from co‐occurrence correlation matrices of measures. We demonstrate the efficiency of the proposed framework on three steganography algorithms designed to hide messages in images represented in the spatial domain: S‐UNIWARD, WOW, and Synch‐HILL. For each algorithm, we applied a range of different payload sizes. The efficiency of the proposed method is demonstrated by the comparison with the previous authors work and the spatial color‐rich model and color filter array‐aware features for steganalysis. Copyright © 2016 John Wiley & Sons, Ltd.

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Content‐adaptive steganalysis for color images

Liao

Chen

Yin

2016

Security Comm Networks

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Some steganography methods for gray-scale image can be extended to true RGB color image by treating each of its three color channels as a gray-scale image. In modern popular steganography, most embedding changes are highly concentrated on those complex textural regions with smaller embedding distortions. However, the existing steganalysis methods for color images directly extract steganalytic features from the whole image. In this paper, we propose a content-adaptive steganalysis strategy for color images. The new strategy aims to extract spatial rich model features from each color channel and just extract color rich model features from the pixels that may have been modified. In order to locate those suspected pixels, we first calculate the embedding costs of each channel, and then a subset of pixels with smaller embedding costs is selected. Experimental results show that the proposed strategy performs better than the state-of-the-art color image steganalysis method.

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Rich model for Steganalysis of color images

Cited by 126 publications

References 19 publications

Color Image Stegananalysis Using Correlations between RGB Channels

Color Image Stegananalysis Using Correlations between RGB Channels

Color images steganalysis using rgb channel geometric transformation measures

Content‐adaptive steganalysis for color images

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