A color image steganography scheme based on texture features

Li, Zhong‐Ming; Deng, Jiayu; Zhu, Lin; Xie, Jiancang; Li, Linxi; Jiang, Yiming

doi:10.1109/ctmcd53128.2021.00010

Cited by 1 publication

(1 citation statement)

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“…Steganalysis is one of the most significant [19], [20]. The reason for this is that the information hidden in the images is very difficult to discover or distinguish with human eye, therefore texture analysis is used to uncover information that the human eye cannot see it [21]. For example, when any image embedding the secret data in an image", the texture and characteristics in an image deviated.…”

Section: Glcm Featurementioning

confidence: 99%

Low feature dimension in image steganographic recognition

Ahmed

Jamil

Hammad

2022

IJEECS

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Steganalysis <span>aids in the detection of steganographic data without the need to know the embedding algorithm or the "cover" image. The researcher's major goal was to develop a Steganalysis technique that might improve recognition accuracy while utilizing a minimal feature vector dimension. A number of Steganalysis techniques have been developed to detect steganography in images. However, the steganalysis technique's performance is still limited due to their large feature vector dimension, which takes a long time to compute. The variations of texture and properties of an embedded image are clearly seen. Therefore, in this paper, we proposed Steganalysis recognition based on one of the texture features, such as gray level co-occurrence matrix (GLCM). As a classifier, Ada-Boost and Gaussian discriminant analysis (GDA) are used. In order to evaluate the performance of the proposed method, we use a public database in our proposed and applied it using IStego100K datasets. The results of the experiment show that the proposed can improve accuracy greatly. It also indicates that in terms of accuracy, the Ada-Boost classifier surpassed the GDA. The comparative findings show that the proposed method outperforms other current techniques especially in terms of feature size and recognition accuracy</span>.

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Section: Glcm Featurementioning

confidence: 99%