Journal of Artificial Intelligence

Year: 2017 | Volume: 10 | Issue: 1 | Page No.: 22-31
DOI: 10.3923/jai.2017.22.31

Reversible Secret Data Hiding Based on Adjacency Pixel Difference

R. Anushiadevi, Padmapriya Praveenkumar , John Bosco Balaguru Rayappan and Rengarajan Amirtharajan

Abstract: Background: There have been discussed several data hiding techniques in an image which can embed the data in secret manner. However, after retrieving the secret data from an image, the original cover image information is not possible to be reconstructed. It is an important issue to have a lossless data hiding scheme which can reconstruct the original cover image without any loss after extracting the secret data. Materials and Methods: In this study a novel idea is introduced for lossless data hiding using adjacency pixel difference. Results: Here histogram is drawn for the pixel difference and secret bits are embedded only in the peak point of the histogram and tested on five gray scale cover images. Conclusion: In this method good amount of secret data can be embedded with high PSNR value.

Keywords: lossless data hiding, histogram shifting, Information security, reversible data hiding and histogram modification

INTRODUCTION

Invertible data secreting is a highly protected technology to implant clandestine data into host picture indiscernibly and enable the host picture to be regained without any fault upon extraction of the implanted clandestine data which gives additional information to the embedded data. This technology is used in digital watermarking techniques where clandestine data and the image are very important.

Many reversible data hiding methodologies were developed in recent years. Difference between two adjacent pixels is used to embed data in difference expansion method¹. A data can be hide using histogram shifting mechanism by Ni et al.². The lossless compression method is used to create extra space to embed data by Celik et al.³. To improves the performance of reversible data hiding various technologies have been introduced by Luo et al.⁴, Hong et al.⁵ and Chang et al.⁶. Amplitudes of DCT coefficients are used to embed the data⁷.

In order to increase the information security Zhang⁸ proposed the lossless data hiding technique in an encrypted image. Zhang⁹ separable lossless data hiding is introduced. Sorting and prediction method is used for lossless watermarking algorithm¹⁰. Ni et al.¹¹ scheme introducing a new embedding mechanism, which increases the data embedding capacity. In this method the image is divided into several numer of sub images and using pathwork theory for each sub image secret data are embeded.

Zeng et al.¹² are introduced new algorithm for reversible data hiding using arithmetic difference. The secret can be easily retreived based on the data distribution by Ni et al.¹¹ and Zeng et al.¹². De Vleeschouwer et al.¹³ and Kim et al.¹⁴ showed better imperceptibility in their algorithm than Ni et al.¹¹ and Zeng et al.¹². Zhao et al.¹⁵ introduced a method called multilevel histogram modification. Huang and Chang¹⁶ introduced algorithm based on quantized coefficients of Discrete Wavelet Transform (DWT).

MATERIALS AND METHODS

Literature survey: Ramaswamy and Arumugam¹⁷ introduced a new lossless data hiding algorithm using adjacency pixel difference. The embedding algorithm steps are given below:

In this proposed method the host image is divided in to several 2×2 sub images. Then adjacency pixel difference is calculated for each block. From this difference peak point is calculated. The payload capacity is based on the peak point level. If the peak point is high then payload capacity is also high. This algorithm retrieves the secret without any loss and the image is also recovered with no distortion.

Proposed scheme: An efficient reversible data hiding method is proposed using histogram shifting is given in Fig. 1. Here adjacency pixel difference is used for embedding. Neighbouring pixel in an image is highly correlated. Hence, maximum adjacency pixel difference is nearly zero as shown in Fig. 2a. Let us consider ‘d’ is the peak point of the histogram and add ‘n’ with each difference which have value greater than ‘d’ and this is called histogram shifting, where ‘n’ value is 3 for 2 bits embedding and for 3 and 4 bits embedding ‘n’ values are 7 and 15, respectively.

Equation 1 and Fig. 2b show the histogram shifting. Histogram shifting algorithm and embedding algorithm are given in this study. Data retrieving and image reconstruction is reverse process of the data embedding and histogram shifting in Eq. 4:

Histogram shifting

Embedding algorithm:

RESULTS AND DISCUSSION

This new lossless data hiding algorithm based on adjacency pixel difference was implemented using jdk1.6. The cover Lena image size 512×512 is given in Fig. 3a. The resultant stego image after embedding is given in Fig. 3b. The recovered image after extracting the secret image is given in Fig. 3c. The corresponding histogram of cover, stego and recovered image of Lena is given in Fig. 3d-f, respectively and observed no difference in histogram level.

The baboon cover and stego images are given in Fig. 4a and b, respectively and recovered baboon image is given in Fig. 4c. Figure 4d-f are shown their corresponding histograms and found to be intact for all the cases original, stego and recovered cover object.

Babara cover, stego and recovered images are given in Fig. 5a-c, respectively and those corresponding histograms are given in Fig. 5d-f, respectively and found to be intact for all the cases original, stego and recovered cover object.

The cameraman cover and stego images are given in Fig. 6a and b, respectively and recovered cameraman image is given in Fig. 6c. Figure 6d-f are shown their corresponding histograms and found to be intact for all the cases original, stego and recovered cover object.

Gold Hill cover and stego images are given in Fig. 7a and b, respectively and recovered Gold Hill image is given in Fig. 7c. Figure 7d-f are shown their corresponding histograms and found to be intact for all the cases original, stego and recovered cover objects.

The man cover and stego images are given in Fig. 8a and b, respectively and recovered man image is given in Fig. 8c. Figure 8d-f are shown their corresponding histograms found to be intact for all the cases original, stego and recovered cover object. The boat cover and stego images are given in Fig. 9a and b, respectively and recovered boat image is given in Fig. 9c.

The butterfly cover and stego images aregiven in Fig. 10a and b, respectively and recovered butterfly image is given in Fig. 10c. Figure 10d-f are shown their corresponding histograms.

The road cover and stego images are given in Fig. 11a and b, respectively and recovered road image is given in Fig. 11c. Figure 11d-f are shown their corresponding histograms.

The manwoman cover and stego images are given in Fig. 12a and b, respectively and recovered manwoman image is given in Fig. 12c.

Figure 12d-f are shown their corresponding histograms and found to be intact for all the cases original, stego and recovered cover objects.

This new lossless data hiding algorithm based on adjacency pixel difference method embedding capacity is superior as compared with other method is shown in Table 1. Pepper (512×512) offer good capacity and PSNR. Butterfly host image (512×512) offer low capacity, the stego object offer a favorable PSNR value for steganography and comparable with baboon (512×512)^2,17.

CONCLUSION

This study gives an overall view about the reversible data hiding technique using histogram modification. This can be achieved by calculating the difference between two neighbouring pixels. Except the edge pixels almost all neighbouring pixels are extremely correlated, this will help to embed huge quantity of secret data when compare to the older ones. For full lossless data retrieving and image recovery is possible to share the peak difference value from the histogram. This technique is successfully compiled and executed using jdk1.8. In the years to come, this powerful algorithm will find a wide range of applications. One such instance is the direction of video sequences where each sequence can be separated into single frames. In each frame this method suggests to apply the same lossless data hiding technique and combine those frames to get the original.

SIGNIFICANCE STATEMENT

REFERENCES