Department of Electronics and Communication Engineering

SREE VIDYANIKETHAN ENGINEERING COLLEGE Sri Sainathnagar, A.Rangampet, Tirupathi-517102

A DWT based Approach for Steganography Using Biometrics by

V.Sreenija (07121A04A3)Y V S G Phani S (07121A04C9)Sagar K (07121A0494)G.Kullaiswamy (08125A0411)K.V.V.Prasad (08125A0412)

A Presentation on

Under the guidance of Prof. P .V .Ramana Professor of ECE

IMAGE HIDING METHODS

OBJECTIVE

• Investigate the use of edge embedding methods.

• Investigate the use of skin tone detection in Steganography.

• Combine edge embedding with skin tone detection to create a

new adaptive Steganography method.

STEGANOGRAPHY

• A Greek word “Covered Writing” Stega covered, from the Greek “stegos” or roof -nography writing, from the Greek “graphia”.

• Steganography is defined as the science of hiding or embedding “data” in a transmission medium.

• Objectives: undetectability, robustness and capacity of the hidden data.

LITERATURE SURVEY

Steganography in Spatial Domain:

Embeds the bits of secret message directly into the LSB plane of the cover image.

Secret data can be easily stolen.

LITERATURE SURVEY

Steganography in Frequency Domain:

Hiding message in noisy regions than in the smoother regions.

For this,Cover image is transformed into frequency domain coefficients using DCT OR DWT.

Different sub-bands give significant information about where vital and non-vital pixels of image resides.

More secure and tolerant to noises.

LITERATURE SURVEY

Modern Steganography

fE: steganographic function "embedding"fE-1: steganographic function "extracting"cover: cover data in which emb will be hiddenemb: message to be hiddenkey: parameter of fEstego: cover data with the hidden message

PROPOSED METHOD

Overview of method is briefly introduced as follows:

• Skin tone detection is performed on input image using HSV colour space.

• Cover image is transformed into frequency domain using Haar-DWT.

• Payload is calculated.

• Cropping the skin region of cover image is done and in that region secret

data is embeded.

• Cropped region works as a key at decoding side.

SKIN COLOR TONE DETECTION

• Colour image is converted into HSV colour space to yield

distinguishble regions of skin or near skin tone.

• Skin pixel is determined by defining a boundary.

SKIN TONE DETECTION

Skin tone detection. (a) Original colour image (b) RGB transformation to gray (c) probable skin regions and (d) edge of (c).

DISCRETE WAVELET TRANSFORM

• DWT is a frequency domain approach in which steganography

is implemented.

• DWT applies on entire image.

• DWT splits component into numerous frequency bands called

sub bands known as LL – Horizontally and vertically low pass LH – Horizontally low pass and vertically high pass HL - Horizontally high pass and vertically low pass HH - Horizontally and vertically high pass

DISCRETE WAVELET TRANSFORM

Advantages of DWT over DCT

• No need to divide the input coding into non-overlapping 2-D blocks, it has higher compression ratios avoiding blocking artifacts.

• Allows good localization both in time and spatial frequency domain.

• Transformation of the whole image introduces inherent scaling

• Better identification of which data is relevant to human perception higher compression ratio

EMBEDDING PROCESS

EXTRACTION PROCESS

PERFORMANCE OF PROPOSED METHOD

• Peak Signal to Noise ratio used to be a measure of image quality

• The PSNR between two images each of size MxN in terms of decibels (dBs) is given by:

• PSNR =   20 * log10 (255 / sqrt(MSE))  • MSE   = 

where I(x,y) is the original image, I'(x,y) is the stego image and M,N are dimensions of image     

• Generally when PSNR is 40 dB or greater, then the original and the reconstructed images are virtually indistinguishable by human observers

DATA ACQUISITION TOOLBOX

Exploring the Toolbox:

• A list of the toolbox functions is available to you by typing help daq

• Toview the code for any function by typing type function_name

• To view the help for any function by typing daqhelp function_name

DATA ACQUISITION TOOLBOX

• A = imread(filename,fmt)

• B = imresize(A,[mrows ncols])

• newmap = rgb2gray(map)

• imwrite(A,filename,fmt)

• imshow(I)

• BW = edge(I,'sobel')

• IM2 = imcomplement(IM)

APPLICATIONS OF STEGANOGRAPHY

Steganography is applicable to, but not limited to, the following

areas.

•   Confidential communication and secret data storing

•   Protection of data alteration

•   Access control system for digital content distribution

•   Media Database systems

CONCLUSION

• Biometric steganography is presented that uses skin

region of images in DWT domain for embedding secret data.

• Image cropping concept is introduced, maintains security at

respectable level since no one can extract message without

having value of cropped region.

REFERENCES

• Shejul, A.A.,Kulkarni, U.L.: A DWT based Approach for Steganography Using Biometrics in Proceedings of the International Conference on Data Storage and Data Engineering,June 2010.

• Digital Image Processing Using MATLAB 2nd Ed. by Gonzalez, Woods, and Eddins