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. 2017 May 12;12(5):e0176979.
doi: 10.1371/journal.pone.0176979. eCollection 2017.

Reversible integer wavelet transform for blind image hiding method

Affiliations

Reversible integer wavelet transform for blind image hiding method

Nazeer Muhammad et al. PLoS One. .

Abstract

In this article, a blind data hiding reversible methodology to embed the secret data for hiding purpose into cover image is proposed. The key advantage of this research work is to resolve the privacy and secrecy issues raised during the data transmission over the internet. Firstly, data is decomposed into sub-bands using the integer wavelets. For decomposition, the Fresnelet transform is utilized which encrypts the secret data by choosing a unique key parameter to construct a dummy pattern. The dummy pattern is then embedded into an approximated sub-band of the cover image. Our proposed method reveals high-capacity and great imperceptibility of the secret embedded data. With the utilization of family of integer wavelets, the proposed novel approach becomes more efficient for hiding and retrieving process. It retrieved the secret hidden data from the embedded data blindly, without the requirement of original cover image.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Fresnelet transformed decomposition of the secret data of Bridge image with key parameter d1 = 1m.
(A) approximation data, (B) the horizontal detail data, (C) the vertical detail data, (D) the diagonal detail data, (E) zoomed-in view of approximation data, (F) zoomed-in view of horizontal detail data, (G) zoomed-in view of vertical detail data, and (H) zoomed-in view of diagonal detail data.
Fig 2
Fig 2. Fresnelet transformed encryption application.
(A) The corresponding approximation data obtained from the Fresnelet transformed application on the Fig 1A with key parameter d2 = 10−4 m, (B) the corresponding horizontal detail data obtained from the Fresnelet transformed application on the Fig 1B, (C) the corresponding vertical detail data obtained from the Fresnelet transformed application on the Fig 1C, (D) the corresponding diagonal detail data obtained from the Fresnelet transformed application on the Fig 1D, (E) the magnitude of complex valued scrambled data, (F) the real part of the scrambled data, (G) the imaginary part of the scrambled data, (H) the reconstructed image using the inverse Fresnelet transform process from the real part and the imaginary part of the scrambled data.
Fig 3
Fig 3. The proposed method processing in flow-chart.
(A) the embedding and (B) the retrieving.
Fig 4
Fig 4. The secret embedded images (carrying secret image Bridge) and recovered input images (lossless recovery after extraction of the secret image).
(A) Embedded image Lena (PSNR = 33.56), (B) embedded image Airplane (PSNR = 31.07), (C) embedded image Mandrill (PSNR = 29.97), (D) embedded image Peppers (PSNR = 23.89), (E) recovered input image Lena, (F) recovered input image Airplane, (G) recovered input image Mandrill, and (H) recovered input image Peppers.
Fig 5
Fig 5. Extracted secret data (Bridge).
(A) Extracted secret data from embedded image Lena, (B) extracted secret data from embedded image Airplane, (C) extracted secret data from embedded image Mandrill, and (D) extracted secret data from embedded image Peppers.
Fig 6
Fig 6. Graphical demonstration of the comparison of Table 4.
(A) Capacity carried by Lena, (B) capacity carried by Airplane, (C) capacity carried by Boat, and (D) capacity carried by Mandrill. (Images information is given in Data Availability Statement.)

References

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