DWT Technique for Steganography
Abstract
Steganography is the hiding of a secret message within an ordinary message and the extraction of it at its destination. Steganography takes cryptography a step farther by hiding an encrypted message so that no one suspects it exists. Ideally, anyone scanning your data will fail to know it contains encrypted data. Additionally secret data embedding is performed using frequency domain approach - DWT (Discrete Wavelet Transform), DWT outperforms than DCT (Discrete Cosine Transform). Secret data is hidden in one of the high frequency sub-band of DWT by tracing skin pixels in that subband. Different steps of data hiding are applied by cropping an image interactively. Cropping results into an enhanced security than hiding data without cropping i.e. in whole image, so cropped region works as a key at decoding side. This study shows that by adopting an object oriented Steganography mechanism, in the sense that, we track skin tone objects in image, we get a higher security. And also satisfactory PSNR (Peak- Signal-to-Noise Ratio) is obtained.
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Introduction
In modern digital steganography, data is first encrypted by the usual means and then inserted, using a special algorithm, into redundant (that is, provided but unneeded) data that is part of a particular file format such as a JPEG image. Think of all the bits that represent the same color pixels repeated in a row. By applying the encrypted data to this redundant data in some random or nonconspicuous way, the result will be data that appears to have the "noise" patterns of regular, nonencrypted data. A trademark or other identifying symbol hidden in software code is sometimes known as a watermark.
Conclusion
Biometric Steganography is presented that usesskin region of images in DWT domain for embedding secret data. By embedding data in only certain region and not in whole image security is enhanced. Also image cropping concept introduced, maintains security at respectable level since no one can extract message without having value of cropped region. Features obtained from DWT coefficients are utilized for secret data embedding. This also increases the quality of Stego because secret messages are embedded in high frequency sub-bands which human eyes are less sensitive too. According to simulation results, proposed approach provides fine image quality.