JOURNAL ARTICLE

Performance Evaluation of Modified Color Image Steganography Using Discrete Wavelet Transform

Vijay KumarDinesh Kumar

Year: 2017 Journal:   Journal of Intelligent Systems Vol: 28 (5)Pages: 749-758   Publisher: IlmuKomputer.Com

Abstract

Abstract Steganography is the foremost influential approach to hide data. Images serve as the most appropriate cover media for steganography. This paper intends to do a performance evaluation of color images and its comparison with the recently proposed approaches, using the modified technique already proposed for grayscale images, by the authors. This approach hides large data in color image using the blocking concept. The blocking process is applied on approximation coefficients of secret image and detail coefficients of red, green and blue components of cover image. The blocks of detail coefficients are replaced with approximation coefficients of secret image using root mean square error method. The key is used to store the position of best matching blocks. It is being predicated that the work will be able to hide large data in a single image. The stego image (ST) has better visual quality based on the peak signal to noise ratio values.

Keywords:
Steganography Peak signal-to-noise ratio Artificial intelligence Computer science Grayscale Color image Image (mathematics) Computer vision Block (permutation group theory) Cover (algebra) Wavelet Image quality Key (lock) Mean squared error Noise (video) Pattern recognition (psychology) Mathematics Image processing Statistics

Metrics

9
Cited By
0.51
FWCI (Field Weighted Citation Impact)
25
Refs
0.69
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced Steganography and Watermarking Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Digital Media Forensic Detection
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Chaos-based Image/Signal Encryption
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
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