International Journal of Science and Engineering Research (IJ0SER), Vol 3 Issue 5 May , (P) X

Transcription

1 Robust and Protected Data Hiding in Natural Image Using Steganography Technique Abha Sharma 1, Prof Shreechandra Upadhyay 2, Prof Rajkumar Paul 3 1 (Research scholar, M.tech in Computer science, VIT Bhopal /RGPV, India) 2 (Deppt. Of Computer science VIT Bhopal/RGPV, India) 3 (HOD, deppt. Of Computer science VIT Bhopal/RGPV, India) Abstract:-In this proposed article we have introduced a new enhanced method for information hiding that is Steganography. This paper is based on the skin tone region of the images. Steganography method use this paper is base do In biometrics. And the biometric feature used to implement steganography skin tone region of images. Here secret data is embedded with in skin region of image that will provide an excellent secure location for data hiding. For this skin tone detection is perform edusinghsv color space.additionally secret data embedding is perform educing frequency domain approach DWT,DWTout Performs than DCT(DiscreteCosineTransform). Secret data is hidden in one of the high frequency sub-band of DWT by tracing skin pixel sin that sub-band..and also satisfactory PSNR(Peak-Signal-to- Noise Ratio) is obtained. Keywords-Stego-image, HSV, DWT (Discrete Wavelet Transform), Skin tone, Steganography. I. INTRODUCTION In many times it is needed not only to convert data into unreadable form but also to hide its existence. For this we can use steganography. Steganography is a branch of information security. The main purpose of steganography is, hiding the existence of the actual communication. in steganography we can hide the actual information into other information in such a way that burglar cannot detect it.in steganography information can be hidden in carriers such as image, audio files, text files and video files. This paper gives a review of image steganography, its various techniques, its advantages and disadvantages and application. A. Image Steganography System In today s world use of computer and internet and transfer of important information through it increasing day by day, for the transferring of such important information, security of information is also necessary.one technique of security of such information is encryption.in encryption information is transformed in such a way that it cannot be detect by hacker. But during encryption; message is changed so it become distorted and intruder may suspect about the presence of important information. Another way of securing the secret information is steganography. Steganography is a branch of information hiding. Steganography application conceal information in other, seemingly innocent media. Steganography comes from greek words staeganos(covered) and graptos(writing).its technical meaning is covered or hidden writing. A block diagram of a generic image steganographic system is given in Fig. 1 A message is embedded in a digital image (cover image) through an embedding algorithm, with the help of a secret key. The resulting stego image is transmitted over a channel to the receiver where it is processed by the extraction algorithm using the same key. During transmission the stego image, it can be monitored by unauthenticated viewers who will only notice the transmission of an image without discovering the existence of the hidden message. Message Message Cover Image Stegosystem Encoder Stegosystem Decoder Fig. 1 Generic form of Image Steganography Stego Image CHANNEL Stego Image Image Steganography Terminologies Image steganography terminologies are as follows:- Cover-Image Original image which is used as a carrier for hidden information. Message Actual information which is used to hide into images. Message could be a plain text or some other image. Stego-Image After embedding message into cover image is known as stego-image. Stego-Key A key is used for embedding or extracting the messages from cover-images and stego-images. Generally image steganography is method of information hiding into cover-image and generates a stego-image. This

2 stego-image then sent to the other party by known medium, where the third party does not know that this stego-image has hidden message. After receiving stego-image hidden message can simply be extracted with or without stego-key (depending on embedding algorithm) by the receiving end [21]. Basic diagram of image (a) Stego Image Generated at Sender Side Each of these techniques is covered in detail in the next two subsections Spatial Domain Techniques - These techniques use the pixel gray levels and their color values directly for encoding the message bits. These techniques are some of the simplest schemes in terms of embedding and extraction complexity. There are many versions of spatial steganography, all directly change some bits in the image pixel values in hiding data. Least significant bit (LSB)- based steganography is one of the simplest techniques that hides a secret message in the LSBs of pixel values without introducing many perceptible distortions. Changes in the value of the LSB are imperceptible for human eyes. Spatial domain techniques are broadly classified into: Least significant bit (LSB) Pixel value differencing (PVD) Edges based data embedding method (EBE) Random pixel embedding method (RPE) Mapping pixel to hidden data method Labeling or connectivity method Pixel intensity based method Texture based method Histogram shifting methods General advantages of spatial domain LSB technique are: 1. There is less chance for degradation of the original image. 2. More information can be stored in an image. (b) Message extraction at receiver side Fig.2. Concept of Steganography Generally image steganography is method of information hiding into cover-image and generates a stego-image. This stego-image then sent to the other party by known medium, where the third party does not know that this stego-image has hidden message. After receiving stego-image hidden message can simply be extracted with or without stego-key (depending on embedding algorithm) by the receiving end [21]. Basic diagram of image steganography is shown in Figure 2 without stego-key, where embedding algorithm required a cover image with message for embedding procedure. Output of embedding algorithm is a stego-image which simply sent to extracting algorithm, where extracted algorithm unhide the message from stego-image. 4. Image Steganographic Techniques- The steganography algorithms proposed in literature can broadly be classified into two categories Spatial Domain Techniques Transform Domain Techniques. Drawbacks of LSB technique are - The major drawback of these methods is amount of additive noise that creeps in the image which directly affects the Peak Signal to Noise Ratio and the statistical properties of the image. Moreover these embedding algorithms are applicable mainly to lossless image compression schemes like TIFF images. For lossy compression schemes like JPEG, some of the message bits get lost during the compression step Transform Domain Techniques These techniques try to encode message bits in the transform domain coefficients of the image. Data embedding performed in the transform domain is widely used for robust watermarking. Similar techniques can also realize largecapacity embedding for Steganography. Candidate transforms include DCT, discrete wavelet transform DWT, and DFT. By being embedded in the transform domain, the hidden data resides in more robust areas, spread across the entire image, and provides better resistance against signal processing. For example, we can perform a block DCT and, depending on payload and robustness requirements, choose one or more components in each block to form a new data group that, in turn, is pseudo randomly scrambled and undergoes a secondlayer transformation. Modification is then carried out on the double transform domain coefficients using various schemes.

3 Discrete Fourier transformation technique (DFT). Discrete cosine transformation technique (DCT). Discrete Wavelet transformation technique (DWT). Lossless or reversible method (DCT) Embedding in coefficient bits PROPOSED METHOD Proposed method introduces a new method of embedding secret data within skin as it is not that much sensitive to HVS (Human Visual System) [2].This takes advantage of Biometrics features such as skin tone, instead of embedding data anywhere in Image, data will be embedded in selected regions. Overview of method is briefly introduced as follows. At first skin tone detection is performed on input image using HSV (Hue, saturation, value) color space. Secondly cover image is transformed in Frequency domain. This is performed by applying Haar-DWT, the simplest DWT on image leading to four sub- bands. Then payload (number of bits in which we can hide data) is calculated. Finally secret data embedding is performed in one of the high frequency sub-band by tracing skin pixels in that band. Before performing a steps Cropping on input image is performed and then in only cropped region embedding is done, not in whole image. Cropping results into more security than without cropping. Since cropped region works as a key at decoding side. Here embedding process affects only certain Regions of Interest (ROI) rather than the entire image. So utilizing objects within images can be more advantageous. This is also called as Object Oriented steganography [2].It is generally divided into four steps:- 1) Skin tone detection 2) Embedding of message 3) Extraction of message 4) Validation Step 1. - In this whole process first step is the Skin tone detection. In the skin tone detection A skin detector typically transforms a given pixel into an appropriate color space and then uses a skin classifier to label the pixel whether it is a skin or a non-skin pixel.]. Color space used for skin detection in this work is HSV. Any color Image of RGB color space can be easily converted into HSV color space. After this the main and important part that is extraction of data or can say secret data at the receiver end. That is the next step of our proposed work. For this again we show a flow chat that is shows the method of extraction of message from stego image. Step 3 - Secret data extraction is explained as follows: 24 bit color stego image of size M N is input to extraction process. We must need value of cropped area to retrieve data. Suppose cropped area value is stored in rect variable that is same as in encoder. Figure Flowchart of Embedding Process Flowchart for extraction Process Step 2 - Embedding of message The second step of the our proposed work is embedding the secret data. All steps are shown in below flow chart for embedding data into aimage.

4 So this rect will act as a key at decoder side. All steps of Decoder are opposite to Encoder. Care must be taken to crop same size of square as per Encoder. By tracing skin pixels in H HH sub-band of DWT secret data is retrieved. Extraction procedure is represented using Flowchart in Figure. Step 4 In last step we have done the validation of this whole process. RESULT In this section focus on proposed method result- the result of the proposed method for the data hiding in skin tone based color as well as gray image in the presence of noise. For the Simulation of our proposed method use a MATLAB-R2012b ( ). Simulate our purposed method we have taken a different digital image they are Baby, Boy, and other images. All images having same and standard size. Testing our planed method for different bit level here we have 8 bit data hiding in different images skin part and calculate the peak signal to noise ratio (PSNR), mean square error (MSE). The formula of parameter is shown in below - And the MSE is state that - PSNR 10log. MSE M N S i1 j1 k1 2 (255) 10 MSE ( Yi j k Yˆ,, i, j, k ) m n s For the calculation of mean square error we have taken three frames of color over there that M for Red, N for the green for S. (a) (b) (c) (d) (e) 2 Table shows the Values of Proposed Method IMAGE PSNR MSE BABYa(56BIT) db BABYb(56BIT) db BABYc(40 BIT) db BABYd(32BIT) db BOYe(32BIT) db CONCLUSION AND FUTURE WORK This paper gave an overview of different steganographic techniques its major types and classification of steganography which have been proposed in the literature during last few years. We have critical analyzed different proposed techniques which show that visual quality of the image is degraded when hidden data increased up to certain limit using LSB based methods. And many of them embedding techniques can be broken or shows indication of alteration of image by careful analysis of the statistical properties of noise or perceptually analysis. REFERENCES [1] Y. K. Jain and R. R. Ahirwal, A Novel Image Steganography Method With Adaptive Number of Least Significant Bits Modification Based on Private Stego-Keys, International Journal of Computer Science and Security (IJCSS), vol. 4, (2010) March 1. [2] H. Yang, X. Sun and G. Sun, A High-Capacity Image Data Hiding Scheme Using Adaptive LSB Substitution, Journal: Radioengineering, vol. 18, no. 4, (2009), pp [3] S.Channalli and A.Jadhav, Steganography an Art of Hiding Data, International Journal on Computer Science and Engineering, IJCSE, vol. 1, no. 3, (2009). [4] C.-H. Yang, C.-Y. Weng, S.-J. Wang, Member, IEEE and H.-M. Sun, Adaptive Data Hiding in Edge Areas of Images with Spatial LSB Domain Systems, IEEE Transactions on Information Forensics and Security, vol. 3, no. (2008) September 3, pp [5] K.-H. Jung, K.-J. Ha and K.-Y. Yoo, Image data hiding method based on multi-pixel differencing and LSB substitution methods, Proc International Conference on Convergence and Hybrid Informa tion Technology (ICHIT '08), Daejeon (Korea), (2008) August 28-30, pp [6] H. Zhang, G. Geng and C. Xiong, Image Steganography Using Pixel-Value Differencing, Electronic Commerce and Security, ISECS '09. Second International Symposium on (2009) May. [7] W.J. Chen, C. C. Chang and T. H. N. Le, High Payload Steganography Mechanism Using Hybrid Edge Detector, Expert Fig. 5 Images use for Data Hiding Process

5 Systems with Applications (ESWA 2010), vol. 37, pp , (2010) April 4. [8] V.MadhuViswanatham and J. Manikonda, A Novel Technique for Embedding Data in Spatial Domain, International Journal on Computer Science and Engineering, IJCSE, vol. 2, (2010). [9] M.A.Al-Husainy, Image Steganography by Mapping Pixels to Letters, Journal of Computer Science, vol.5, no. 1, (2009), pp [10] H. Motameni, M. Norouzi, M. Jahandar and A. Hatami, Labeling Method in Steganography, World Academy of Science, Engineering and Technology, France, (2007). [11] Capacity Filter Based Steganography, International Journal of Recent Trends in Engineering, vol. 1, no. 1, (2009) May. [12] M. TanvirParvez and A. Abdul-Aziz Gutub, RGB Intensity Based Variable-Bits Image Steganography, IEEE Asia-Pacific Services Computing Conference, (2008), pp [13] A. M. Hamid and M. L. M. Kiah, Novel Approach for High Secure and High Rate Data Hidden in the Image Using Image Texture Analysis, International Journal of Engineering and Technology (IJET): , (2009). [14] M. Chaumont and W. Puech, DCT-Based Data Hiding Method To Embed the Color Information in a JPEG Grey Level Image, 14th European Signal Processing Conference (EUSIPCO 2006), Florence, Italy, copyright by EURASIP, (2006) September 4-8. [15] T. H. Manjula Devi, K. R. Venugopal and L. M. Pataki, Authentication of secret information in image steganography, IEEE Region 10 Conference, TENCON- 2008, (2008) November, pp [16] S. Ohyama, M. Niimi, K. Yamawaki and H. Noda, Lossless data hiding using bit depth embedding for JPEG2000 compressed bit -stream, Journal of Communication and Computer, vol. 6, no. 2, (2009) February. [17] NF.Johnson and S. Katzenbeisser, A Survey of steganographic techniques. in Information Hiding Techniques for Steganography and Digital Watermarking,S.Katzenbeisser and F.Petitcolas, Ed. London: Artech House, (2000), pp [18] H. S. Majunatha Reddy and K. B. Raja, (2009) High capacity and security steganography using discrete wavelet transform. International Journal of Computer Science and Security. pp [19] Steganography. in Information Hiding Techniques for Steganography and Digital Watermarking, S. Katzenbeisser and F. Petitcolas, Ed. London: Artech House, (2000), pp [20] P.Kruus,C.Scace, M.Heyman, and M. Mundy., A survey of steganography techniques for image files.advanced Securit y Research Journal. [On line], 5(1), (2003), pp [21] N. Johnson and S. Jajodia, Exploring steganography: seeing the unseen, IEEE Computer, pp , February(1998). [22] Pfitzmann, B., Information hiding terminology - results of an informal plenary meeting and additional proposals. In: Proceedings of the First International Workshop on Information Hiding. Springer - Verlag, London, UK, pp (1996). [23] Review of Data Hiding in Digital Images. Proceedings of the Image Processing, Image Quality, Image Capture Systems Conference (PICS'99), Savannah, Georgia, April 25-28, (1999). [24] Handel, T. &Sandford, M., Hiding data in the OSI network model, Proceedings of the 1st International Workshop on Information Hiding, June (1996). Abha Sharma has completed her B.E in computer science and now doing postgraduation in computer science & application from Vedica Institute of Technology (VIT) Bhopal (M.P.).