51400025-ppt-3
DESCRIPTION
sTRANSCRIPT
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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
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IMAGE HIDING METHODS
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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.
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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.
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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.
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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.
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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
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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.
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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.
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SKIN TONE DETECTION
Skin tone detection. (a) Original colour image (b) RGB transformation to gray (c) probable skin regions and (d) edge of (c).
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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
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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
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EMBEDDING PROCESS
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EXTRACTION PROCESS
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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
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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
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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)
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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
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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.
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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