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Table 1.

The embedding domain for the existing state of the art methods.

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Table 2.

The combined spatial and frequency domains with different embedding techniques for the existing state of the art methods.

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Fig 1.

Relationship of the correlation metric to the texture features (a) contrast, (b) energy, (c) homogeneity, (d) entropy, (e) correlation, (f) mean, and (g) standard deviation for Lena image.

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Fig 1 Expand

Fig 2.

Relationship of the MSE metric to the texture features (a) contrast, (b) energy, (c) homogeneity, (d) entropy, (e) correlation, (f) mean, and (g) standard deviation for Lena image.

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Fig 2 Expand

Fig 3.

Relationship of the SSIM metric to the texture features (a) contrast, (b) energy, (c) homogeneity, (d) entropy, (e) correlation, (f) mean, and (g) standard deviation for Lena image.

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Fig 3 Expand

Fig 4.

Relationship of the correlation metric to the texture features (a) contrast, (b): energy, (c) homogeneity, (d) entropy, (e) correlation, (f) mean, and (g) standard deviation for Sails image.

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Fig 4 Expand

Fig 5.

Relationship of the MSE metric to the texture features (a) contrast, (b) energy, (c) homogeneity, (d) entropy, (e) correlation, (f) mean, and (g) standard deviation for Sails image.

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Fig 5 Expand

Fig 6.

Relationship of the SSIM metric to the texture features (a) contrast, (b) energy, (c) homogeneity, (d) entropy, (e) correlation, (f) mean, and (g) standard deviation for Sails image.

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Fig 6 Expand

Table 3.

Trends of the imperceptibility to the texture feature for the Lena and Sails images.

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Table 3 Expand

Fig 7.

Construction of data set and feature domain.

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Fig 7 Expand

Table 4.

RMSEs for the training phase and testing phase for different images.

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Table 4 Expand

Table 5.

Accuracy levels of the different training data set percentages for the Lena, Sails and Baboon images.

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Table 5 Expand

Fig 8.

Training data set percentage dependent variation of Corr for the Lena, Sails, and Baboon images.

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Fig 8 Expand

Fig 9.

Training data set percentage dependent variation of MSE for the Lena, Sails, and Baboon images.

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Fig 9 Expand

Fig 10.

Training data set percentage dependent variation of SSIM for the Lena, Sails, and Baboon images.

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Fig 10 Expand

Fig 11.

Training data set percentage dependent variation of fusion1 for the Lena, Sails, and Baboon images.

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Fig 11 Expand

Fig 12.

General framework of the proposed OELF model.

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Fig 12 Expand

Fig 13.

Achieved host (left) and stego (right) images.

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Table 6.

RMSE values obtained using the ELM model for various images.

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Table 6 Expand

Table 7.

Comparison of the OELF model results with other existing models.

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Table 7 Expand

Fig 14.

Performance of the proposed imperceptibility metric (fusion2).

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Fig 14 Expand