Steganography using Concept of Skin Tone Detection

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1 Steganography using Concept of Skin Tone Detection Miss.Snehal Manjare*, Dr.Mrs.S.R.Chougule** *(Department of E&TC, Shivaji University, Kolhapur ** (Department of E&TC, Shivaji University, Kolhapur Abstract Steganography is the art of hiding the existence of data in another transmission medium to achieve secret communication. Steganography method used in this paper is based on biometrics. And the biometric feature used to implement Steganography is skin tone region of images.in this paper, describes an implementation for skin detection which relies on the HSV(Hue, Saturation and Value) color space model. In the detected skin area we are able to hide the secret data. Due to skin color variation from zero to one; any change in skin color is not affecting original image and also human visual system. 1. INTRODUCTION Steganography or Stego as it is often referred to in the IT community, literally means, "covered writing" which is derived from the Greek language. Steganography is defined by Markus Kahn [5] as follows, "Steganography is the art and science of communicating in a way which hides the existence of the communication. In contrast to Cryptography, where the enemy is allowed to detect, intercept and modify messages without being able to violate certain security premises guaranteed by a cryptosystem, the goal of Steganography is to hide messages inside other harmless messages in a way that does not allow any enemy to even detect that there is a second message present". In a digital world, Steganography and Cryptography are both intended to protect information from unwanted parties. Both Steganography and Cryptography are excellent means by which to accomplish this but neither technology alone is perfect and both can be broken. It is for this reason that most experts would suggest using both to add multiple layers of security. Steganography can be used in a large amount of data formats in the digital world of today. The most popular data formats used are.bmp,.doc,.gif,.jpeg,.mp3,.txt and.wav. Mainly because of their popularity on the Internet and the ease of use of the steganographic tools that use these data formats. These formats are also popular because of the relative ease by which redundant or noisy data can be removed from them and replaced with a hidden message. Steganographic technologies are a very important part of the future of Internet security and privacy on open systems such as the Internet. Steganographic research is primarily driven by the lack of strength in the cryptographic systems on their own and the desire to have complete secrecy in an open-systems environment. Many governments have created laws that either limit the strength of cryptosystems or prohibit them completely. This has been done primarily for fear by law enforcement not to be able to gain intelligence by wiretaps, etc. This unfortunately leaves the majority of the Internet community either with relatively weak and a lot of the times breakable encryption algorithms or none at all. Civil liberties advocates fight this with the argument that these limitations are an assault on privacy. This is where

2 Steganography comes in. Steganography can be used to hide important data inside another file so that only the parties intended to get the message even knows a secret message exists. To add multiple layers of security and to help subside the "crypto versus law" problems previously mentioned, it is a good practice to use Cryptography and Steganography together. But in this paper instead of cryptography, layer of security increased by using skin part of cover image where secret data is to be hidden. Detection of the human skin part is an essential step in many computer vision and biometric applications such as automatic face recognition, video surveillance, human computer interaction (HCI) and large -scale face image retrieval systems. The first step in any of these face processing systems is the detection of the presence and subsequently the position of human faces in an image or video. The main challenge in face detection is to cope with wide variety of variations in the human face such as face pose and scale, face orientation, facial expression, ethnicity and skin colour. External factors such as occlusion, complex backgrounds, inconsistent illumination conditions and quality of the image may also contribute significantly to the overall problem. Throughout the last decade, there has been much development in face detection research, particularly in the abundance of methods and approaches. Recent surveys [1], [2] have comprehensively reviewed various face detection methods available in the literature. Face detection in colour images has also gained much attention in recent years. Colour is known to be a useful cue to extract skin regions, and it is only available in colour images. This allows easy face localisation of potential facial regions without any consideration of its texture and geometrical properties. Most techniques up to date are pixel-based skin detection methods [3], which classifies each pixel as skin or non-skin individually and independently from its neighbours. Early methods use various statistical colour models such as a single Gaussian model [4], Gaussian mixture density model [5], and histogram-based model [6]. Some colour spaces have their luminance component separated from the chromatic component, and they are known to possess higher discriminality between skin pixels and nonskin pixels over various illumination conditions. Skin colour models that operate only on chrominance subspaces such as the Cb- Cr [7], [8], [9] and H-S [10] have been found to be effective in characterising various human skin colours. 2. PROPOSED WORK Encoding Secret Messages in Images: Coding secret messages in digital images is by far the most widely used of all methods in the digital world of today. This is because it can take advantage of the limited power of the human visual system (HVS). Almost any plain text, cipher text, image and any other media that can be encoded into a bit stream can be hidden in a digital image. With the continued growth of strong graphics power in computers and the research being put into image based Steganography, this field will continue to grow at a very rapid pace. Proposed method introduces a new method of embedding secret data within skin region as it is not that much sensitive to HVS (Human Visual System) [1].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. Then payload (number

3 of bits in which we can hide data) is calculated. Finally, secret data embedding is performed in one of the high frequency subband by tracing skin pixels in that band. All these embedding steps are applied to Cropped cover image. Cropped region works as a key at decoding side so cropping results into more security. Ultimately it is observed that this algorithm provide security and efficiency. In this case 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 [1]. Next sub-sections describe encoding, decoding process in detail and briefly introduce skin tone detection. Skin detection of cover image Applied Steganography Recovering the cover image Fig 1: Block diagram of the proposed work. 3. SKIN TONE DETECTION In images, skin color is an indication of human and human like existence. Therefore, in the last 20 years extensive research have focused on skin detection in images and its uses in detecting face and non-face like features. Skin detection using color information can be a challenging task as the skin appearance in images is affected by various factors such as illumination, background, camera characteristics, and ethnicity. Numerous techniques are presented in literature for skin detection using color. Skin-color detection in visible spectrum can be a very challenging task as the skin color in an image is sensitive to various factors such as: 1) There is color constancy problem that means indoor, outdoor, shadows; highlight produces a change in the skin color of images. So it is very important problem which seriously affect the performance of skin detection task. 2) Skin color also varies from person to person belonging to different ethnic groups and from persons across different regions 3) The skin-color distribution for the same person differs from one camera to another depending on the camera sensor Characteristics. 4) Individual characteristics such as age, sex and body parts also affect the skin-color appearance. 5) Different factors such as subject appearances (makeup, hairstyle and glasses), background colors, shadows and motion also influence skin-color appearance [4]. Different methods of skin detection have been proposed by many researchers. Methods of Skin Detection-1) Pixel-Based Method-Classify each pixel as skin or non-skin individually, independently from its neighbours. Color Based Methods fall in this category.2) Region Based Methods. Here pixel based method is used for detecting the skin part of the image. After getting the skin region, facial features viz. Eyes and Mouth are extracted. The image obtained after applying skin color statistics is subjected to binarization i.e., it is transformed to grayscale image and then to a binary image by applying suitable threshold. This is done to eliminate the hue and saturation values and consider only the luminance part. This luminance part is then transformed to binary image with some threshold because the features we want to consider further for face extraction are darker than the background colors. After thresholding, opening

4 and closing operations are performed to remove noise. These are the morphological operations, opening operation is erosion followed by dilation to remove noise and closing operation is dilation followed by erosion which is done to remove holes. Now we extract the eyes, ears and mouth from the binary image by considering the threshold for areas which are darker in the mouth than a given threshold. Threshold is predefined range associated with the target skin pixel values. Most of the researchers determined threshold as h_range = [0, 0.11] and s_range = [0.2, 0.7]. Sobottaka and Pitas [11] defined a face localization based on HSV. They found that human flesh can be an approximation from a sector out of a hexagon with the constraints: Smin= 0.23, Smax =0.68, Hmin =00 and Hmax= STEGANOGRAPHY EMBEDDING PROCESS Here we must need a value of cropped area to retrieve data. Suppose cropped area value is stored in rect variable that is same as in encoder. So this rect will act as a key at decoder side. Care must be taken to crop same size of square as per Encoder. By tracing skin pixels in HHH sub-band of DWT secret data is retrieved. The secret images are of size We use Peak signal to noise ratio (PSNR) to evaluate quality of stego image after embedding the secret message. The performance in terms of capacity and PSNR (in db) is demonstrated for the method in the following subsections. PSNR is defined as per Equations: 10log10 (255 2/ MSE) Where, MSE = (1/ (M N)) Σ Σ (xij - yij) 2 i=1 j=1 xij and yij represents pixel values of original cover image and stego image respectively. 5. RESULTS Fig. 2 fig. 3 fig. 4 fig. 5 Fig. 2 cover image, Fig 3 secret image, Fig 4 Stego image, Fig 5 retrieved secret image. For with cropping case, after embedding secret data in cropped image, resulted cropped stego image which should be merged with original image on the bases of cropped area key is shown in fig.2. For merging co-ordinates of first and last pixels of cropped image in original image are calculated. After performing decoding process on stego image obtained in with cropping case, retrieved image is shown in Fig. 5 PSNR is calculated for final stego image resulted from a considered image. PSNR for with cropping case is Performing biometric Steganography with cropping offers respectable level of security. With cropping case ensures security as decoder requires cropped region for secret data extraction, so cropped region works as a key at decoder side. 6. CONCLUSION Digital Steganography is a fascinating scientific area which falls under the umbrella of security systems. In this paper Biometric Steganography is presented that uses skin region of images.

5 By embedding data in only certain region (here skin region) and not in whole image security is enhanced. This case for data embedding is considered, with cropping. It is ob served that it offers enough security. In with cropping case no one can extract message without having value of cropped region. According to simulation results, proposed approach provides fine image quality. REFERENCE [1] A. Cheddad, J. Condell, K. Curran and P. Mc Kevitt, Biometric inspired digital image Steganography, in: Proceedings of the 15 th Annual IEEE International Conference and workshops on the Engg.of Computer-Based Systems (ECBS 08) Belfast, 2008, pp [2] Petitcolas, F.A.P.: Introduction to Information Hiding. In Katzenbeisser, S and Petitcolas, F.A.P (ed.) (2000) Information hiding Techniques for Steganography and Digital Watermarking. Norwood: Artech House, INC. [3] Johnson, N. F. and Jajodia, S.: Exploring Steganography: Seeing the Unseen. IEEE Computer, 31 (2): 26-34, Feb [4] Chris Shoemaker, Hidden Bits: A Survey of Techniques for Digital Watermarking Independent Study EER- 290 Prof Rudko Spring 2002 [5] Fridrich, J., Goljan, M. and Du, R.., (2001). Reliable Detection of LSB Steganography in Grayscale and Color Images. Proceedings of ACM, Special Session on Multimedia Security and Watermarking, Ottawa, Canada, October 5, 2001, pp [6] Po-Yueh Chen and Hung-Ju Lin A DWT Based Approach for Image Steganography, International Journal of Applied Science and Engineering, , 3: [7] Chang, C. C., Chen, T.S and Chung, L. Z., A steganographic method based upon JPEG and quantization table modification, Information Sciences, vol.[4], pp (2002). [8] Provos,N. and Honeyman, P: Hide and Seek: An introduction to steganography. IEEE security and privacy, 01 (3): 32-44,May-June 2003 [9] Ahmed E., Crystal M. and Dunxu H.: Skin Detection-a short Tutorial, Encyclopedia of Biometrics by Springer- Verlag Berlin Heidelberg 2009 [10] Yang, J., & Waibel, a. (1996). A real - time face tracker. Proceedings of the 3th IEEE Workshop on Applications of Computer Vision, Sarasota, Florida, [11] Sobottka, K. and Pitas, I.: Extraction of facial regions and features using color and shape information. Proc. IEEE International Conference on Image Processing, pp (1996) [12] Chen,P. Y.and Liao,E.C., :A new Algorithem for Haar Wavelet Transform, 2002 IEEE International Symposium on Intelligent Signal Processing and Communication System, pp (2002). [13] P. Raviraj and M.Y. Sanavullah, The Modified 2D-Haar Wavelet Transformation in Image Compression, Middle-East Journal of Scientific Research 2 (2): 73-78, 2007 ISSN IDOSI Publications, 2007

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