Final Project Report

IMAGE BASED STEGANOGRAPHY USING LSB INSERTION TECHNIQUE A Project report Submitted in partial fulfillment of the requirement For the award of the degree of BACHELOR OF TECHNOLOGY IN ELECTRONICS ELECTRONICS AND COMMUNICATION ENGINEERING ENGINEERING By

K. Sai Pranitha

(07FE1A0419)

T. Usha Sri

(07FE1A0423)

M. V. R. Amaralingeswara Rao V. Kanaka Rao

(08FE5A0417) (08FE5A0418)

Under the guidance of

Mr. M. Suman Assistant Professor

Department of Electronics and Communication Engineering Engineering VIGNANS LARA INSTITUTE OF TECHNOLOGY AND SCIENCE VADLAMUDI  522 213 GUNTUR (Dt.) (2010-11)

CERTIFICATE This is to certify that this project report entitled IMAGE BASED STEGANOGRAPHY USING LSB INSERTION TECHNIQUE is a bonafide record of work done by

K. Sai Pranitha

(07FE1A0419)

T. Usha Sri

(07FE1A0423)

M. V. R. Amaralingeswara Rao

(08FE5A0417)

V. Kanaka Rao

(08FE5A0418)

Under my guidance and supervision and submitted in partial fulfillment of the requirements for the award of the degree of Bachelor of Technology Technology in Electronics Electronics and Communication Engineering by the Jawaharlal Nehru Technological Technological University, Kakinada.

M. SUMAN Assistant Professor

M. V. H. BHASKARA MURTHY Professor and HOD

Department of Electronics and Communication Engineering Engineering VIGNANS LARA INSTITUTE OF TECHNOLOGY AND SCIENCE VADLAMUDI  522 213 GUNTUR (Dt.) (2010-11)

ACKNOWLEDGEMENT

We would like to take this opportunity to express our profound sense of gratitude to our guide Mr. M. SUMAN, Assistant Professor, Vignans Lara Institute of Technology and Science, for his constant guidance, supervision, motivation and encouragement all the way during the project, his annotations and criticisms are the key behind successful completion of this project work. We also like to thank our beloved Head of the Department, Electronics and Communication Engineering, Prof. M. V. H. BHASKARA MURTHY garu, for his cooperation and encouragement in completing this project by providing us proper lab facilities. We, the members of the project, express thanks to the people who were directly or indirectly involved in this project for their overwhelming cooperation. Finally we would like to extend our heartfelt thanks to our beloved parents whose blessings and encouragement were always there as a source of strength and inspiration.

Project Associates

K. Sai Pranitha (0FE91A0419) T. Usha Sri (07FE1A0423) M.V. R. Amaralingeswara Rao (08FE5A0417) V. Kanaka Rao (08FE5A0418)

CONTENTS Abstract

i

List of Figures

ii

1.

INTRODUCTION

1

2.

STEGANOGRAPHY TECHNIQUES

2

2.1 Kerk off Principle

2

STEGANOGRAPHY METHODS

3

3.

3.1

Substitution Method

3.2 Proposed Method

3 4

3.3

Visual Perception

4

3.4

Difference Analysis

5

3.5

Distortion Analysis

5

3.6

Depth Vs Distortion Analysis

5

3.7 Steganography in Images

6

STEGANOGRAPHY DIAGRAMATIC FLOW

7

4.1 Images

7

4.2 Image Compression

8

IMAGE ENCODING TECHNIQUE

9

5.1 Least Significant Bit Insertion

9

5.2 Advantages of LSB

10

5.3 Masking and Filtering

10

5.4 Algorithms and Transformations

11

6.

SYSTEM DESIGN

13

7.

SECURITY

14

8.

FUTURE SCOPE

15

9.

CONCLUSION

16

10.

REFERENCES

17

4.

5.

I

ABSTRACT We propose a new method for strengthening the security of information through a combination of signal processing, cryptography and steganography. Cryptography provides the security by concealing the contents and steganography provides security by concealing existence of information being communicated. Signal processing adds additional security by compressing and transforming the information. The proposed method, viz. Steg anography Based Information Protection Method (SBIPM), consists of scanning, coding, encryption, reshaping, cover processing and embedding steps. We then turn to data-hiding in images. Steganography in images has truly come of age with the invention of fast, powerful computers. Software is readily available off the I nternet for any user to hide data inside images. These software are designed to fight illegal distribution of image documents by stamping some recognizable feature into the image. The most popular technique is Least Significant Bit insertion, which we will look at. Also, we look at more complex methods such as masking and filtering, and algorithms and transformations, which offer the most robustness to attack, such as the Patchwork method which exploits the human eye's weakness to luminance variation. We will take a brief look at steganalysis, the science of detecting hidden messages and destroying them. We conclude by finding that steganography offers great potential for s ecuring of data copyright, and detection of infringers. Soon, through steganography, personal messages, files, all artistic creations, pictures, and songs can be protected from piracy.

Image Based Steganography Using LSB Insertion Technique

2010-2011

II

LIST OF FIGURES 1.

Frame work of Steganography Based Information Protection Method

4

2. Scheme of Steganography

5

3. Depth Vs Distortion Analysis

6

4. Steganography Diagrammatic Flow

7


2010-2011

P ag e |1

1. INTRODUCTION Now a days, various modes of communication like LAN, WAN and INTERNET are widely used for communicating information from one place to another around the globe. Such communication networks are open which any one can access easily. They are regularly monitored and an intercepted. Steganography, from the Greek, means covered or secret writing, and is a long-practiced form of hiding information. Although related to cryptography, they are not the same. Steganography's intent is to hide the existence of the message, while cryptography scrambles a message so that it cannot be understood. More precisely, '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 secret message present." Steganography includes a vast array of techniques for hiding messages in a variety of media. Among these methods are invisible inks, microdots, digital signatures, covert channels and spread-spectrum communications. A message is embedded in a cover media in an invisible manner so that one could not suspect about its existence. In this paper we present a substitution based information protection method where we combine cryptographic, steganographic and signal processing concepts together for achieving security. The method is known as Steganography Based Information Protection method. In this method we substitute the information bit in randomly selected pixels at random places within LSB region.


2010-2011

P ag e |2

2. STEGANOGRAPHY TECHNIQUE Steganography is the art and science of communicating in a way which hides the existence of the secret message communication. It aims to hide information /covered writing. Information to be protected is hidden in another data known as cover or carrier. Data containing hidden message are called as Steganos or Stegos. Steganos look like cover data and it is difficult to differentiate between them. Steganography based communication over easily accessible platforms to prevent leakage of information.

2.1 Kerck Off Principle: In cryptography, this principle states that "the security of the system has to be based on the assumption that the enemy has full knowledge of the design and implementation details of the steganographic system". The only missing information for the enemy is a short, easily exchangeable random number sequence, the secret key.


2010-2011

P ag e |3

3. STEGANOGRAPHY METHODS According to modification in covers, the methods can be categorized as i.

Substitution

ii.

Transform domain

iii.

Spread spectrum

iv.

Statistical

v.

Distortion

vi.

Cover generation

3.1 Substitution Method It is commonly used simple method in which we can put information bits in LSB sequentially at fixed place, randomly at fixed place or randomly at random places in cover pixels. The message to be protected passes through scanning, coding, encryption process to form an embedded message. Scanning, coding, encryption steps make the information unintelligible so that one cannot extract plain message. Embedding make the message invisible so that one cannot detect it. Reshaping spreads the message so that embedded message can be detected from distorted steganos by authorized receivers. Cover processing makes detection of embedded message more difficult since the distortion is either due to noise addition or due to message embedding. This would increases the robustness and security. Many attacks on such steganographic systems are suggested. Some attacks that can be applied are given below: a. Stego-Only Attack b. Message-Stego Attack c. Cover-Stego Attack d. Message-Cover-Stego Attack


2010-2011

P ag e |4

3.2 Proposed Method The framework of proposed Steganography Based Information Protection method is shown in Fig 1. Its description is presented in the following steps.

FRAME WORK OF SBIPM

Encryption Key. Other Ke s

Embedding Algorithm

Cover Work

Stego

Information to Hide Fig 1: Frame Work of Steganography Based Information Protection Method

3.3 Visual Perception For any steganography based secure system, the perception of steganos should be as cover image itself so that one cannot differentiate them and detect the existence of embedded message.


2010-2011

P ag e |5

Fig 2: Scheme of Steganography

3.4 Difference Analysis The "difference-images obtained by taking the difference between cover or processed cover and stego image are invisible. For making the difference visible in "difference-images, for visual interpretation, we first increase differences by multiplication of weight factor and then revert the values to get strengthened "difference-images". From analysis of these "diffe renceimages ", on could not say that the changes are ei ther due to cover processing or message embedding and hence we can say that the method is safe f rom known cover-stego attack.

3.5 Distortion Analysis Distortion analysis of stego images is carried out by studying distortion / similarity messages statistically. There are many methods for measuring distortion that can be used for distortion analysis. Distortion between two different images is measured by considering Mean Square Error (MSE), Mean Absolute Error (MAE) o r Histogram Similarity (HS).

3.6 Depth Vs Distortion Analysis Distortion occurred in different steganos is required by varying the depth of hiding for embedding information in cover image. The relation between depth of hiding used and distortion occurred in the stego images is shown in Fig. t hat depth of hiding within some LSB region is most suitable for message embedding as the distortion is very small in this region. As the depth of hiding increases beyond preferable region, the distortion becomes noticeable and unsuitable for message hiding. Image Based Steganography Using LSB Insertion Technique

2010-2011

P ag e |6

Fig 3: Depth Vs Distortion Analysis

3.7 Steganography in Images In this section we deal with data encoding in still digital images. In essence, image steganography is about exploiting the limited powers of the human visual system (HVS). Within reason, any plain text, cipher text, other images, or anything that can be embedded in a bit stream can be hidden in an image. When embedding data, it is important to remember the following restrictions and features: y

The cover data should not be significantly degraded by the embedded data, and the embedded data should be as imperceptible as possible. (This does not mean the embedded data needs to be invisible; it is p ossible for the data to be hidden while it remains in plain sight.)

y

The embedded data should be directly encoded into the media, rather than into a header or wrapper, to maintain data consistency across formats.

y

The embedded data should be as immune as possible to m odifications from intelligent attacks or anticipated manipulations such as filtering and resampling. Some distortion or degradation of the embedded data can be expected when the cover data is modified. To minimize this, error correcting codes should be used. The embedded data should be self-clocking or arbitrarily re-entrant. This ensures that the embedded data can still be extracted when only a portion of the cover data is available. For example, if only a part of image is available, the embedded data should still be recoverable.

4. STEGANOGRAPHY DIAGRAMAT1C FLOW


2010-2011

P a g e | 7

Fig 4: Steganography Diagrammatic Flow

4.1 Images To a computer, an image is an array of numbers that represent light intensities at various points, or pixels. These pixels make up the image's raster data. An image size of 640 by 480 pixels, utilizing 256 colors (8 b its per pixel) is fairly common. Such an image would contain around 300 kilobits of data. Digital images are typically stored in either 24-bit or 8-bit per pixel fades. 24-bit images are sometimes known as true color images. Obviously, a 24-bit image provides more s pace for hiding information; however, 24-bit images are generally large and not that common. A 24-bit image 1024 pixels wide by 768 pixels high would have a size in excess of 2 megabytes. As such, large files would attract attention were they to be transmitted across a network or the Internet. Image compression is desirable. However, compression brings with it other problems that shall be explained shortly.


2010-2011

P a g e | 8

Alternatively, 8-bit color images can be used to hi de information. In 8-bit color images, (such as GIF files), each pixel is represented as a single byte. Each pixel merely points to a color index table, or palette, with 256 possible colors. The pixel's value, then, is between 0 and 255. The image software merely needs to paint the indicated color on the screen at the selected pixel position. If using an 8-bit image as the cover-image, many steganography experts recommend using images featuring 256 shades of gray as the palette, for reasons that will become apparent. Grey-scale images are preferred because the shades change very gradually between palette entries. This increases the image's ability to hide i nformation. When dealing with 8-bit images, the steganographer will need to consider the image as well as the palette. Obviously, an image with large areas of solid color is a poor choice, as variances created by embedded data might be noticeable. Once a suitable cover image has been selected, an image encoding technique needs to be chosen.

4.2 Image Compression Image compression offers a solution to large image files. Two kinds of image compression are lossless and lossy compression. Both methods save storage space but have differing effects on any uncompressed hidden data in the image. Lossy compression, as typified by JPEG (Joint Photographic Experts Group) format files, offers high compression, but may not maintain the original image's integrity. This can impact negatively on any hidden data in the image. This is due to the lossy compression algorithm, which may "lose" unnecessary image data, providing a close approximation to high-quality digital images, but not an exact duplicate. Hence, the term" Tossy" compression. Lossy compression is frequently used on true-color images, as it offers high compression rates. Lossless compression maintains the original image data exactly; hence it is preferred when the original information must remain intact. It is thus more favoured by steganographic techniques. Unfortunately, lossless compression does not offer such high compression rates as lossy compression. Typical examples of lossless compression formats are CompuServes GIF (Graphics Interchange Format) and Microsoft's BMP (Bitmap) format.

5. IMAGE ENCODING TECHNIQUES Image Based Steganography Using LSB Insertion Technique

2010-2011

P ag e |9

Information can be hidden many different ways in images. Straight message insertion can be done, which will simply encode every bit of information in the image. More complex encoding can be done to embed the message only in " noisy" areas of the image that will attract less attention. The message may also be scattered randomly throughout the cover image. The most common approaches to information hiding in images are: y

Least significant bit (LSB) insertion

y

Masking and filtering techniques

y

Algorithms and transformations

Each of these can be applied to various images, with v arying degrees of success. Each of them suffers to varying degrees from operations performed on images, such as cropping, or resolution decrementing, or decreases in the color depth.

5.1 Least Significant Bit Insertion One of the most common techniques used in steganography today is called least significant bit (LSB) insertion. This method is exactly what it sounds like; the least s ignificant bits of the cover-image are altered so that they form the embedded information. The following example shows how the letter A can be hidden in the first eight bytes of three pixels in a 24-bit image.

Pixels: (00100111 11101001 11001000) (00100111 11001000 11101001) (11001000 00100111 11101001)

A:

10000001

Result: (00100111

11101000 11001000)

(00100110 11001000 11101000) (11001000 00100111 11101001)

The three underlined bits are the only three bits that were actually altered. LSB insertion requires on average that only half the bits in an image be changed. Since the 8-bit letter A only


2010-2011

P a g e | 10

requires eight bytes to hide it in, the ninth byte of the three pixels can be used to hide the next character of the hidden message. A slight variation of this technique allows for embedding the message in two or more of the least significant bits per byte. This increases the hidden information capacity of the coverobject, but the cover-object degrades more statistically, and it is more detectable. Other variations on this technique include ensuring that statistical changes in the image do not occur. Some intelligent software also checks for areas that are made up of one so lid color. Changes in these pixels are then avoided because slight changes would cause noticeable variations in the area.

5.2 Advantages of LSB Insertion Major advantage of the LSB algorithm is it is quick and easy. There has also been steganography software developed which work

around LSB color alterations via palette

manipulation. LSB insertion also works well with gray-scale images. A slight variation of this technique allows for embedding the message in two or more of the least significant bits per byte. This increases the hidden information capacity.

5.3 Masking and Filtering Masking and filtering techniques hide information by marking an image in a manner similar to paper watermarks. Because watermarking techniques are more integrated i nto the image, they may be applied without fear of image destruction from lossy compression. By covering, or masking a faint but perceptible signal with another to make the f irst nonperceptible, we exploit the fact that the human visual system cannot detect slight changes in certain temporal domains of the image. Technically, watermarking is not a steganographic form. Strictly, steganography conceals data in the image; watermarking extends the image information and becomes an attribute o f the cover image, providing license, ownership or copyright details. Masking techniques are more suitable for use in lossy JPEG images than LSB insertion because of their relative immunity to image operations such as compression and cropping.

5.4 Algorithms and Transformations Image Based Steganography Using LSB Insertion Technique

2010-2011

P a g e | 11

Because they are high quality color images with good compression, it is desirable to use JPEG images across networks such as the Internet. Indeed, JPEG images are becoming abundant on the Internet. JPEG images use the discrete cosine transform (DCT) to achieve compression. DCT is a lossy compression transform, because the cosine values cannot be calculated precisely, and rounding errors may be introduced. Variances between the original data and the recovered data depend on the values and methods used to calculate the DCT. Images can also be processed using fast Fourier transformation and wavelet transformation. Other properties such as luminance can also be utilized. The H VS has a very low sensitivity to small changes in luminance, being able to discern changes of no less than one part in thirty for random patterns. This figure goes up t o one part in 240 for uniform regions of an image. Modern steganographic systems use spread-spectrum communications to transmit a narrowband signal over a much larger bandwidth so that the spectral density of the signal in the channel looks like noise. The two different spread-spectrum techniques these tools employ are called directsequence and frequency hopping. The former hides information by phase-modulating the data signal (carrier) with a pseudorandom number sequence that both the sender and the receiver know. The latter divides the available bandwidth into multiple channels and hops between these channels (also triggered by a pseudorandom number sequence). The Patchwork method is based on a pseudorandom, statistical process that takes advantage of the human weaknesses to luminance v ariation. Using redundant pattern encoding to repeatedly scatter hidden information throughout the cover image, like a patchwork, Patchwork can hide a reasonably small message many times in a image. I n the Patchwork method, n pairs of image points (a, b) are randomly chosen. The brightness of a is decreased by one and the brightness of b is increased by one. For a labeled image, the expected value of the sum of the differences of the n pairs of points is then 2n. Bender shows that after JPEG compression, with the quality factor set to 75, the message can still be decoded with an 85. This algorithm is more robust to image processing such as cropping and rotating, but at the cost of message size. Techniques such as Patchwork are ideal for watermarking of images. Even if the image is cropped, there is a good probability that the watermark will still be readable. Other techniques encrypt and scatter the hidden throughout the image in some predetermined manner. It is assumed that even if the mess age bits are extracted, they will be Image Based Steganography Using LSB Insertion Technique

2010-2011

P a g e | 12

useless without the algorithm and stego-key to decode them. Although such techniques do help protect against hidden message extraction, they are not immune to destruction of the hidden message through image manipulation.


2010-2011

P a g e | 13

6. SYSTEM DESIGN

These are the steps followed in image hiding while transmission and de-noising after receiving: 1.

Step1: Get a cover image (publicly accessible material)

2. Step2: Take the information to be hidden (message or image) 3. Step3: Combine cover image with the information to be hidden(we follow LSB algorithm) 4. Step4: While transmission it will be corrupted by noise 5. Step5: Use any of the filtering methods, ex: wiener filtering for de noising in wavelet domain 6. Step6: Here filter is employed in order to remove the noise 7. Step7: During extraction a password check is provided 8. Step8: If password is matched then extraction of hidden information.


2010-2011

P a g e | 14

7. SECURITY A method, SBIPM, for providing the security of our important information is based on the techniques of signal processing, cryptography, and steganography. The security of information has been strengthened by applying scanning, coding, and encryption, cover processing and embedding techniques in the method. Reshaping step of the method provides robustness for detecting message correctly in such situation when stego image is distorted. The method developed is safe from various attacks. Simulation and steganalysis results shown in this paper infer that one will not be able to distinguish between cover and stego images. Thus we conclude that the strength of security achieved is very h igh and unauthorized receiver will not be able to get back the original message using even after exhaustive methods without the knowledge of key parameters. Digital Steganography is interesting field and growing rapidly for information hiding in the area of information security. It plays a vital r ole in defense as well as civil applications. In future we will more of secure systems based on this technology. Several methods for hiding data in, images were described, with appropriate introductions to the environments of each medium, as well as the strengths and weaknesses of each method. The key algorithm for designing the steganography system has been dealt. Most data-hiding systems take advantage of human perceptual weaknesses, but have weaknesses of their own. We conclude that for now, it seems that no system of data-hiding is totally immune to attack. However, steganography has its place in security. Though it cannot replace cryptography totally, it is intended to supplement it. Its application in watermarking and fingerprinting, for use in detection of unauthorized, illegally copied material, is continually being realized a nd developed.


2010-2011

P a g e | 15

8. FUTURE SCOPE In this report many relevant issues were presented, from a technical point of view. However, little has been done to motivate these studies. A more detailed investigation of applications, and a comparison with current techniques in steganography would have been interesting. For example, a thorough evaluation of the advantages natural language-based techniques can offer over image-based techniques could have offered valuable insights. An important contribution of this project to natural language steganography is the linguistic sophistication of the model for word-substitution put forward. The lexical models employed in current substitution-based systems were often criticized and their inadequate behavior usually described with respect to language theory. T hese phenomena could have been demonstrated by example, showing texts and inadequate replacements carried out by current stego-systems. A more detailed analysis of how common these critical situations really are in typical text could have given clues for the construction of such systems, to decide whether the additional complexity introduced by statistical word-sense disambiguation is worth the e ffort. Other linguistic models have been studied, in addition to the lexical o nes, and put in relation to each other, and to their use for steganographic purposes. The steganographic aspects were then covered by information-theoretic models. However, little has been done to justify this choice. It might have been fruitful to present other characterizations of steganography and to compare their suitability to natural language steganography. A central part of the problem motivating this report was that there are n o models formalizing the design and analysis of natural language stegosystems. Although the present report somewhat improves the situation, by providing a systematic investigation of the topic, there is still no system to build upon for making formal claims about security or robustness in the natural language scenario.


2010-2011

P a g e | 16

9. CONCLUSION Several methods for hiding data in, images were described, with appropriate introductions to the environments of each medium, as well as the strengths and weaknesses of each method. The key algorithm for designing the steg anography system has been dealt. Most data-hiding systems take advantage of human perceptual weaknesses, but have weaknesses of their own. We conclude that for now, it seems that no system of data-hiding is totally immune to attack. However, steganography has its place in security. Though it cannot replace cryptography totally, it is intended to supplement it. Its application in watermarking and fingerprinting, for use in detection of unauthorized, illegally copied material, is continually being realized a nd developed. Also, in places where standard cryptography and encryption is outlawed, steganography can be used for covert data transmission. Steganography can be used al ong with cryptography to make a highly secure data high way. Formerly just an interest of the military, Steganography is now gaining popularity among the masses. Soon, any computer user will be able to put his own watermark on his artistic creations. Thus we conclude that the strength of security achieved is very h igh and unauthorized receiver will not be able to get back the original message using exhaustive without the knowledge of key parameters. Digital Steganography is interesting field and growing rapidly for information hiding in the area of information security. It plays a vital r ole in defense as well as civil applications.


2010-2011

P a g e | 17

10. REFERENCES [ 1] B.Schneier, Terrorists and Steganography, 24 Sep. 2001,

http://www.zdnet.com/zdnn/stories/comment/0,5859,2814256,00.html [ 2] Y. Linde, A. Buzo, and R. M. Gray, An Algorithm for Vector Quantizer Design, IEEE Transactions on

Communications, pp. 84-95, January 1989 [3] Andersen, R.J., Petitcolas, F.A.P., On the limits of steganography. IEEE Journal of Selected Areas in

Communications, Special Issue on Copyright and Privacy Protection 16 No.4 (1998) [ 4] Johnson, Neil F. and Jajodia, Sushil. Steganography: Seeing the Unseen. IEEE Computer February

1998, pp.2634. [ 5] William Stallings; Cryptography and Network Security: Principals and Practice, Prentice Hall

international, Inc.; 2002. [ 6] Eric Cole ,"Hiding in Plain Sight: Steganography and the Art of Covert Communication" [7 ] Gregory Kipper,"Investigator's Guide to Steganography " [ 8] Stefan Katzenbeisser and Fabien, A. P. Petitcolas ," Information Hiding Techniques for Steganography

and Digital Watermarking " [ 9] Hiding secrets in computer files: steganography is the new invisible ink, as codes stow away on

images-An article from: The Futurist by Patrick Tucker [ 10] Ismail Avcbas¸, Member, IEEE, Nasir Memon,Member, IEEE, and Bülent Sankur, Member,

"Steganalysis Using Image Quality Metrics," IEEE Transactions on I mage Processing, Vol 12, No.2, February 2003 [ 11] Niels Provos and Peter Honeyman, University of Michigan, "Hide and Seek: A n Introduction to

Steganography" IEEE Computer Society IEEE Security &Privacy. [ 12] R. Chandramouli and Nasir Memon, "Analysis of LSB Based Image Steganography Techniques",

IEEE 2001.


2010-2011

Final Project Report

Recommend Documents