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Design and Implementation of LSB based Minimum Deviation Steganography technique on High Payload Grayscale Images
Arnab Pal1, Aritra Bandyopadhyay2

1Arnab Pal*, Department of Computer Science and Engineering, Supreme Knowledge Foundation Group of Institutions, Mankundu, India.
2Aritra Bandyopadhyay, Department of Computer Science and Engineering, Supreme Knowledge Foundation Group of Institutions, Mankundu, India. 
Manuscript received on November 14, 2019. | Revised Manuscript received on 23 November, 2019. | Manuscript published on December 10, 2019. | PP: 538-542 | Volume-9 Issue-2, December 2019. | Retrieval Number: B6510129219/2019©BEIESP | DOI: 10.35940/ijitee.B6510.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The paper discusses the design of a new steganographic method for 8-bit grayscale images. Here, the input cover image is decomposed into 2 × 2 blocks of pixels which are non-overlapping in row major order. Each block can embed 7- bits from secret bit-stream. Successive block embedding operation ensures the concealment of the entire hidden information into the carrier image. The proposed method offers a fixed payload of 1.75 bits per pixel (bpp) which is considered to be a high payload in the field of Steganography. The degradation of the stego-image is also not severe in our method and that can be analyzed by observing the Peak Signal to Noise Ratio (PSNR) of greater than 30 dB. In the reverse way, the receiver decomposes the cover image into 2 × 2 non-overlapping blocks of pixels in row major order. Successive extraction of 7- secret bits from each block ensures the re-generation of the secret information. Simulation results ensure that the proposed method is superior than other schemes in terms of qualitative clarity of the Stego-image. 
Keywords: Data Hiding, Minimum Deviation Steganography, Grayscale Image, LSB
Scope of the Article: Image Processing and Pattern Recognition