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A Daubechies DWT Based Image Steganography Using Smoothing Operation
Author(s) -
Vijay Kumar Sharma,
Devesh Kumar Srivastava,
Pratistha Mathur
Publication year - 2019
Publication title -
the international arab journal of information technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.227
H-Index - 27
eISSN - 2309-4524
pISSN - 1683-3198
DOI - 10.34028/iajit/17/2/2
Subject(s) - steganography , computer science , cover (algebra) , artificial intelligence , embedding , image (mathematics) , discrete wavelet transform , image quality , wavelet , computer vision , smoothing , scrambling , transformation (genetics) , digital image , wavelet transform , algorithm , image processing , mechanical engineering , biochemistry , chemistry , engineering , gene
Steganography is a capability which conceals the top-secret information into cover media (e.g., digital images, sound files etc.,). This Paper presents a secure, higher embedding capacity Discrete Wavelet Transformation (DWT) based technique. Before embedding correlation in between cover and the secret image is increased by multiplying some variable (i.e., 1/k) to the secret image. In embedding process, the Daubechies DWT of both Arnold transformed secret and cover images are taken followed by alpha blending operation. Arnold is a type of scrambling process which increases the confidentiality of secret image and alpha blending is a type of mixing operation of two images, the alpha value indicates the amount of secret image is embedded into the cover image. Daubechies Inverse Discrete Wavelet Transformation (IDWT) of the resulting image is performed to obtain the stego image. Smoothing operation inspired by the Genetic Algorithm (GA) is used to improve the quality of stego-image by minimizing Mean square error and morphological operation is used to extract the image component from the extracted secret image. Simulation results of the proposed steganography technique are also presented. The projected method is calculated on different parameters of image visual quality measurements.

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