Implementation of Lsb Steganography and Its Evaluation for Various File Formats

Int. J. ripe(p) Ne dickensrking and Applications leger 02, write 05, Pages 868-872 (2011) 868 Implementation of LSB Steganography and its Evaluation for Various File Formats V. Lokeswara Reddy Department of CSE, K. S. R. M. College of Engg. , Kadapa, A. P. India Email emailprotected com Dr. A. Subramanyam Dept. of CSE, AITS, Rajampet, Y. S. R. (Kadapa) Dist.. A. P. Dr. P. Chenna Reddy Dept. of CSE, JNTUCE, Pulivendula, Y. S. R. (Kadapa) Dist.. A. P. ABSTRACT-Steganography is derived from the Greek enounce steganos which literally means Covered and graphy means Writing, i. e. surmounted writing. Steganography refers to the science of invisible communication. For wipet confidential in initializeion in various file puts, there exists a large variety of steganographic techniques some atomic number 18 to a greater extent(prenominal) complex than other(a)s and all of them experience respective strong and weak points.The least(prenominal) Significant micro chip (LSB) embedd ing technique suggests that data can be surreptitious in the least significant compositions of the cover symbol and the human eye would be unable to notice the unfathomable cypher in the cover file. This technique can be utilize for concealing throws in 24-Bit, 8-Bit, Gray outgo format. This paper explains the LSB Embedding technique and Presents the valuation for various file formats. Keywords Steganography, Least Significant Bit (LSB), GIF, PNG, BMP. -Date of Submission 24, August 2010 Date of Acceptance 08 November 2010 ar transferred through unknown cover carriers in such a manner that the very existence of the introduce nubs is undetec shelve. Carriers include personas audio, video, text or any other digitally delineate code or transmission. The hidden kernel may be plaintext, cipher text or anything that can be represented as a smudge stream.II. IMAGE STEGANOGRAPHY run into compression techniques argon extensively utilize in steganography. Among the two type s of range of mountains compressions, lossy compression and loss less compression lossless compression formats offer more promises. Lossy compression compression may not maintain the original motion-picture shows integrity. lossless compression maintains the original image data exactly, hence it is prefered. Example of Lossy compression format is JPEG format files. Examples of Lossless compression formats are GIF3 and BMP formats.We have utilise an 8- s image size for implementation of our steganography. Improvement in stegnographic techniques is make it possible to apply the Detecting LSB Steganography in Colour and Gray- Scale Images which were confined to gray eggshell images in the initial stages The difficulty in distort images control is solved later on in many techniques such as the analysis of the variation of the gradient energy. The incomprehensible center embedded in the tar give rise image is detected in both gray and colour images, and the length of the embedded message is estimated 5, 6. -I. INTRODUCTION Digital content is now posing formidable challenges to content developers, aggregators, distri simplyors and users. The destruction, extraction or variety of the embedded message is required to develop more robust systems so that the digital content treat and organization becomes easy. Cryptography was created as a technique for securing the concealing of communication and many different methods have been developed to encrypt and decrypt data in order to keep the message secret.Unfortunately it is sometimes not enough to keep the contents of a message secret, it may in like manner be undeniable to keep the existence of the message secret. The technique used to implement this, is called steganography. The shift from cryptography to stegnography is due to that concealing the image existence as stegno-images enable to embeded the secret message to cover images. Steganography conceptually implies that the message to be transmitted is no t visible to the informal eye. Steganography has been used for thousands of years to transmit data without being intercepted by unwanted viewers.It is an art of hiding information inside information. The main objective of Steganography is mainly concerned with the shield of contents of the hidden information. Images are ideal for information hiding1,2 because of the large amount of redundant space is created in the storing of images. Secret messages Int. J. Advanced Net working and Applications Volume 02, hump 05, Pages 868-872 (2011) III. HIDING METHODS IN IMAGE STEGANOGRAPHY In Image Steganography, on that point are a variety of methods exploitation which information can be hidden in images.Least Significant Bit heir technical schoolnique In image steganography al just about all data hiding techniques try to alter insignificant information in the cover image. Least significant turn of events (LSB) interpolation is a common, simple approach to embedding information in a cove r image. For instance, a simple final cause proposed, is to place the embedding data at the least significant bit (LSB) of each pixel in the cover image7,8,9 . The alter image is called stego-image. Altering LSB doesnt change the quality of image to human perception but this scheme is sensitive a variety of image processing attacks like compression, cropping etc.We issue be emphasizing more on this technique for the various image formats. Moderate Significant Bit Replacement Technique The moderate significant bits of each pixel in the cover image can be used to embed the secret message. This method improves sensitivity to modification, but it degrades the quality of stego-image. Experiments have shown that the length of hidden messages embedded in the least significant bits of signal samples can be estimated with relatively high precision. IV. THE LSB proficiency The least significant bit i. e. the eighth bit inside an image is changed to a bit of the secret message.When using a 24bit image, one can store 3 bits in each pixel by changing a bit of each of the red, green and blue colour components, since they are each represented by a byte. An 800? 600 pixel image, can thus store a total amount of 1,440,000 bits or 180,000 bytes of embedded data. As an example, suppose that we have three adjacent pixels (9 bytes) with the RGB encoding. 10010101 00001101 11001001 10010110 00001111 11001011 10011111 00010000 11001011 When the number 300, can be which binary representation is 100101100 embedded into the least significant bits of this part of the image.If we overlay these 9 bits over the LSB of the 9 bytes above, we get the following (where bits in bold have been changed) 10010101 00001100 11001000 10010111 00001110 11001011 10011111 00010000 11001010 Here the number 300 was embedded into the grid, only the 5 bits needed to be changed according to the embedded message. On average, only half of the bits in an image will need to be modified to hide a secret messa ge using the maximum cover size. Since there are 869 256 possible intensities of each primary colour, changing the LSB of a pixel results in dispirited changes in the intensity of the colour.The human eye cannot perceive these changes thus the message is successfully hidden. With a happy image, one can tear down hide the message in the LSB without noticing the difference10. Fig. 1 Block plot for use Logic of LSB embedding V. DESIGN DETAILS This section focuses on algorithmic rules Steganography and Steganalysis10 A. Algorithm for Hiding (Steganography) 1. 2. 3. Read the original image and the image which is to be hidden in the original image Shift the image to hide in the cover image by X bits. And the original image or cover image with 240 which is 11110000 So four MSBs set to 0.Because of this only four LSBs considered further. The shifted hidden image and the result of step 3 are bitored. This makes changes only in the X LSB bits so that the image is hidden in the original image. of LSB 4. In MATLAB we convert it to unit8 format. This image can be called as the stego image B. Algorithm for Steganalysis 1. The stego image is bit shifted by 4 bits since it was shifted by 4 bits to insert it into the original image. 2. The image is the ANDED with 255 i. e. , 11111111, which gives the original image. It is ANDED with 255 because initially all the LSBs were made 0.Now it is recovered back. 3. To get it to Unit8 format we, convert it back to unit8 which is the extracted image. Int. J. Advanced Networking and Applications Volume 02, Issue 05, Pages 868-872 (2011) that a message is being passed is being achieved. C. LSB in GIF 870 Fig. 2 Block Diagram for Steganalysis VI. IMAGE ANALYSIS A. LSB in BMP The BMP file format in any case called bitmap or DIB file format (for device-independent bitmap), is an image file format used to store bitmap digital images. Since BMP is not widely used the mistrust might arise, if it is transmitted with an LSB stego.When im age are used as the carrier in Steganography they are generally manipulated by changing one or more of the bits of the byte or bytes that make up the pixels of an image. The message can be stored in the LSB of one colour of the RGB value or in the parity bit of the entire RGB value. A BMP is capable of hiding quite a large message. LSB in BMP is most able for applications, where the focus is on the amount of information to be transmitted and not on the secrecy of that information. If more number of bits is altered, it may result in a larger possibility that the altered bits can be recognisen with the human eye.But with the LSB the main objective of Steganography is to pass a message to a receiver without an interloper even knowing that a message is being passed is being achieved. B. LSB in PNG Portable Network graphics (PNG) is a bitmapped image format that employs lossless data compression. PNG was created to improve upon and replace GIF. Since PNG is widely used the suspicion m ight not arise if it is transmitted with an LSB stego. When images are used as the carrier in Steganography they are generally manipulated by changing one or more of the bits of the byte or bytes that make up the pixels of an image.The message can be stored in the LSB of one colour of the RGB value or in the parity bit of the entire RGB value . A PNG is capable of hiding quite a large message. LSB in PNG is most suitable for applications where the focus is on the amount of information to be transmitted and not on the secrecy of that information. If more number of bits is altered it may result in a larger possibility that the altered bits can be seen with the human eye. But with the LSB the main objective of steganography i s to pass a message to a receiver without an interloper even knowingGraphics interchange format also known as GIF is one of the machine independent compressed formats for storing images. Since GIF images only have a bit depth of 8, amount of information that can be hidden is less than with BMP. Embedding information in GIF images using LSB results in almost the same results as those of using LSB with BMP. LSB in GIF is a very efficient algorithm to use when embedding a reasonable amount of data in a grayscale image. GIF images are indexed images where the colours used in the image are stored in a pallet.It is sometimes referred to as a colour search table. Each pixel is represented as a single byte and the pixel data is an index to the colour palette. The colours of the palette are typically ordered from the most used colour to the least used colours to reduce lookup time. Some extra care is to be taken if the GIF images are to be used for Steganography. This is because of the problem with the palette approach. If the LSB of a GIF image is changed using the palette approach, it may result in a completely different colour. This is because the index to the colour palette is changed.The change in the resulting image is discernible if the ad jacent palette entries are not similar. But the change is not noticeable if the adjacent palette entries are similar. Most applications that use LSB methods on GIF images have low security because it is possible to detect even moderate change in the image. Solutions to these problems could be 1. Sort the palette so that the colour difference betwixt consecutive colours is minimized 2. Add new colours, which are visually similar to the existing colours in the palette. 3. Use Gray scale images.In a 8 bit Gray scale GIF image, there are 256 shades of gray. This results in gradual changes in the colours and it is hard to detect. VII. EXPERIMENTED RESULTS Following observational results highlights on 8 bit LSB Steganography. A. Results for . png image 8 bit stego image Int. J. Advanced Networking and Applications Volume 02, Issue 05, Pages 868-872 (2011) 871 B. Results for . bmp file 8 bit stego image PSNR is measured in decibels (dB). PSNR is a good measure for analyse restoration re sults for the same image, but between-image comparisons of PSNR are meaningless.MSE and PSNR values for each file format is shown in table 1. Table 1 Image quality metrics for bmp file Cover image 224. 948 24. 6100 Stego image 244. 162 24. 2540 Cover- Stego image 69. 826 29. 690 MSE PSNR Stego Recovered IX. EVALUATION OF DIFFERENT TECHNIQUES There are many steganographic algorithms ready(prenominal). One should select the best available algorithm for the given application. Following characteristics are to be evaluated while selecting a particular file format for Steganography. Steganography says that the secret message is to be hidden and it should result in an distortion less image.The distortion moldiness(prenominal) not be visible to the human eye. The amount of data embedded in the image also plays an important role. The algorithm decides how much amount of data could be embedded in the image resulting in a distortion less image. Steganalysis is the technique of detecting the hidden information in the image. The algorithm for Steganography must be such that the steganalysis algorithms should fail. i. e the Steganography algorithms must not be prune to attacks on steganalysis. During communication the intruder could check the original image to remove the hidden information..He/she may manipulate the image. This manipulation may include cropping or rotation etc of the images. The manipulations done may cause the image distortion. Steganographic algorithms chosen must be such that it overcomes such manipulation and the steganographic data reaches the destination in the required format. VIII. EVALUATION OF IMAGE character For comparing stego image with cover results requires a measure of image quality, commonly used measures are Mean-Squared Error, boot signal/noise ratio Ratio3 and histogram. A.Mean-Squared Error The mean-squared error (MSE) between two images I1(m,n) and I2(m,n) is M and N are the number of rows and columns in the input images, respecti vely. Mean-squared error depends strongly on the image intensity scaling. A mean-squared error of 100. 0 for an 8-bit image (with pixel values in the range 0-255) looks dreadful but a MSE of 100. 0 for a 10- bit image (pixel values in 0,1023) is barely noticeable B. Peak Signal-to-Noise Ratio Peak Signal-to-Noise Ratio (PSNR) avoids this problem by scaling the MSE according to the image range Int. J.Advanced Networking and Applications Volume 02, Issue 05, Pages 868-872 (2011) Table 2 Comparison of LSB technique for various file formats LSB LSB LSB In BMP in GIF In PNG fate Distortion less High Medium High resultant image Invisibility Steganalysis detection Image manipulation Amount of embedded data Payload capacity Independent of file format X. finish Since BMP uses lossless compression, LSB makes use of BMP image. To be able to hide a secret message inside a BMP file, one would require a very large cover image. BMP images of 800? 600 pixels found to have less network application s.Moreover such uses are not accepted as valid. For this reason, LSB Steganography has also been developed for use with other image file formats. Although only some of the main image steganographic techniques were discussed in this paper, one can see that there exists a large selection of approaches to hiding information in images. All the major image file formats have different methods of hiding messages, with different strong and weak points respectively. LSB in GIF images has the potential of hiding a large message, but only when the most suitable cover image has been chosen.High abject junior-grade High High Low Medium Low Low Medi um Medi um Low Medium Low Low Medium Medium High Authors Biography 872 Steganography Seeing the Unseen, Computer Journal, February 1998. 5 Li Zhi,Sui Ai Fen. , Detection of Random LSB Image Steganography The IEEE 2003 internationalistic Symposium on Persona1,lndoor and Mobile Radio Communication Proceedings, 2004. 6 Jessica Fridrich, Miroslav Golja n, and Rui Du. , Detecting LSB Steganography in Color and GrayScale Images, IEEE Multimedia. 7 F. Collin,Encryptpic, http//www. winsite. com/bin/ Info? 500000033023. 8 G. Pulcini, Stegotif, http//www. geocities. om /SiliconValley/9210/gfree. html. 9 T. Sharp, Hide 2. 1, 2001,www. sharpthoughts. org. 10 Deshpande Neeta, Kamalapur Snehal, Daisy Jacobs Implementation of LSB Steganography and Its Evaluation for Various Bits Digital development Management, 2006 maiden International conference. pp 173-178,2007. V. Lokeswara Reddy did his M. Tech (CSE) from SRM University, Chennai in the year 2005. He did his M. C. A from S. V. University, Tirupati in the year 2000. He is move his Ph. D from JNTUA, Anantapur. He has a total of 09 years of experience in teaching. Currently he is working as Associate Professor at K.S. R. M College of Engineering, Kadapa. He has presented 2 papers in International and National Conferences. Dr. A. Subramanyam received his Ph. D. degree in Computer schol arship and Engineering from JNTU College of Engineering, Anantapur. He has obtained his B. E. (ECE) from University of Madras and M. Tech. (CSE) from Visweswaraiah Technological University. He is having 17 years experience in teaching. He is currently working as Professor & HOD in the Department of Computer Science and Engineering of Annamacharya Institute of Technology & Sciences, Rajampet, Y. S. R. (Kadapa) Dist. A. P.He has presented and published number of papers in International and National Conferences, International and National Journals. He is guiding few Ph. D. s. His research areas of bet are parallel processing, network security and data warehousing. Dr. P. Chenna Reddy did his B. Tech (CSE) from S. V. University College of Engineering, Tirupati in the year 1996. He did his M. Tech from JNTU, Anantapur. He completed his Ph. D from JNTU, Hyderabad. He has a total of 13 years of experience in teaching. Currently he is working as Associate Professor at JNTUA College of Engi neering, Pulivendula, Y.S. R. (Kadapa) Dist. , A. P. He has number of publications to his credit. References 1 Pfitzmann Birgit. Information Hiding Terminology, First International Workshop, Cambridge, UK, Proceedings, Computer Science, 1174. pp. 347-350, MayJune. 2 Westfield Andreas and Andreas Pfitzmann, Attacks on Steganographic Systems, Third International Workshop, IH99 Dresden Germany, October Proceedings, Computer Science 1768. pp. 61- 76, 1999. 3 Moerland, T. , Steganography and Steganalysis, Leiden Institute of Advanced Computing