1. VISVESVARAYA TECHNOLOGICAL
UNIVERSITY
JnanaSangama, Belgaum-590014
A Technical Seminar Report
On
“An Adaptive LSB-OPAPbased Secret Data Hiding”
Submitted in Partial fulfillment of the requirements for VIII semester
Bachelor of Engineering
in
Electronics & Communication Engineering
by
TEJAS.S
(1AR09EC043)
Under the Guidance of
Prof. PADMAJA VIJAYKUMAR
Dept. of ECE, AIeMS
DEPARTMENT OF ELECTRONICS AND COMMUNICATION
ENGINEERING
AMRUTA INSTITUTE OF ENGINEERING &
MANAGEMENT SCIENCES
Near bidadi industrial Area, Bengaluru-562109

2. B.V.V.Sangha’s
AMRUTA INSTITUTE OF ENGINEERING AND
MANAGEMENT SCIENCES
Near Bidadi Industrial Area, Bengaluru– 562109
DEPARTMENT OF ELECTRONICS & COMMUNICATION ENGINEERING
CERTIFICATE
This is to Certify that the technical seminar entitled “An Adaptive LSB-OPAP based
Secret Data Hiding”has been carried out byTEJAS.S (1AR09EC043), a bonafide
student of Amruta institute of engineering & management sciences, in the partial
fulfillment of the requirements for the award of the degree in Bachelor of Engineering in
Electronics & Communication Engineering under Visvesvaraya Technological
University, Belgaum during the academic year 2012-2013. It is certified that all
corrections/suggestions indicated for Internal Assessment have been incorporated in the
report deposited in the department library.
Prof. PADMAJA VIJAYKUMAR Prof. C.R RAJAGOPAL
Dept of ECE, AIeMS HOD of ECE, AIeMS
Name of the Examiners: Signature and Date
1.
2.

3. ACKNOWLEDGEMENT
The satisfaction and euphoria that accompany the successful completion of any task
would be incomplete without the mention of the people who made it possible, whose constant
guidance and encouragement crowned my effort with success.
I am grateful to our institution, Amruta Institute of Engineering &
managementSciences (AIeMS) with its ideals and inspirations for having provided me with the
facilities, which made this, seminar a success.
I earnestly thank Dr. A. PRABHAKAR, Principal, AIeMS, for facilitating academic
excellence in the college and providing me with congenial environment to work in, that
helped me in completing this seminar.
I wish to extend my profound thanks to Prof. C.R.RAJAGOPAL, Head of the
Department, Electronics & Communication Engineering, AIeMS for giving me the
consent to carry out this seminar.
I would like to express my sincere thanks to our Internal Guide Prof. PADMAJA
VIJAYKUMAR, Department of Electronics & Communication Engineering, AIeMS for
her able guidance and valuable advice at every stage of my seminar, which helped me in the
successful completion of the seminar.
I wish to express my solicit thanks to my friend Mr. RAGHU.K for his help and
support to my seminar.
I am thankful to all the faculty members and non-teaching staff of the department for
their kind co-operation.
I also wish to thank my friends for their useful guidance on various topics. Last but
not the least, I would like to thank my parents and almighty for the support.
TEJAS.S
(1AR09EC043)

4. ABSTRACT
In the present digital world, Steganography and cryptography are excellent means by
which secret communication can be achieved significantly over the data network. The
classical methods of steganography such as LSB substitution involve hiding the data in a
multimedia carrier. The present research activities are focused on embedding the data and
simultaneously achieving good PSNR and efficient payload. An adaptive method for LSB
substitution with private stego-key based on gray-level ranges is proposed. This new
technique embeds binary secret data in 24-bits colour image or in 8-bits gray-scale image. In
this method the cover image pixels are grouped into 4 ranges based on their intensity levels.
Different ranks are allotted to each of the range so that the range having highest number of
pixel count gets the highest rank and the pixels under this range are embedded with
maximum of 4 bits of secret data. The pixel after embedding may or may not be within the
same range, hence this algorithm proposes an optimum pixel adjustment process (OPAP).
The method also verifies that whether the attacker has tried to modify the secret data
hidden inside the cover image. Besides, the embedded confidential information can be
extracted from stego-images without the assistance of original image. This method provides a
capacity of 3.5 bits/pixel and a PSNR of 52 dB on an average.

5. LIST OF FIGURES
page
Fig 1.1 Classification of Steganography 1
Fig 2.1 Method for k- bits insertion 6
Fig 3.1 LSB – OPAP 7
Fig 4.1 Message embedding with signature 10
Fig 4.2 Message extraction and integrity check 11
Fig 6.1 Experimental result using Range1 for Baboon cover image 14
Fig 6.2 Experimental result using Range2 for Lena cover image 15

6. TABLE OF CONTENTS
Page
1. Introduction to Steganography 1
2. An Adaptive LSB-OPAP employed pixel domain stegotechnique
(ALOS) 4
2.1 Proposed Methodology
2.2 Private stego-key generation
2.3 Method to decide Bits insertion in each range
2.4 LSB substitution
3. Optimum Pixel Adjustment Process (OPAP) 7
4. Implementation of ALOS 9
4.1 Algorithms: Embedding
4.2 Algorithms: Extracting
5.Advantages& Applications of proposed system 12
5.1 Advantages
5.2 Limitations
5.3 Applications
6. Experimental results and discussions 13
References

7. CHAPTER 1
INTRODUCTION TO STEGANOGRAPHY
Steganography is derived from the Greek for covered writing and essentially means
“to hide in plain sight”. Steganography is the art and science of communicating in such a way
that the presence of a message cannot be detected. Simple steganographic techniques have
been in use for hundreds of years, but with the increasing use of files in an electronic format
new techniques for information hiding have become possible.
Figure1.1 shows how information hiding can be broken down into different areas.
Steganography can be used to hide a message intended for later retrieval by a specific
individual or group. In this case the aim is to prevent the message being detected by any other
party.
Figure1.1 Classification of Steganography
Steganography and encryption are both used to ensure data confidentiality. However
the main difference between them is that with encryption anybody can see that both parties
are communicating in secret. Steganography hides the existence of a secret message and in
the best case nobody can see that both parties are communicating in secret. This makes
steganography suitable for some a task for which encryption isn’t, such as copyright marking.

8. Adding encrypted copyright information to a file could be easy to remove but
embedding it within the contents of the file itself can prevent it being easily identified and
removed.
Steganography provides a means of secret communication which cannot be removed
without significantly altering the data in which it is embedded. The embedded data will be
confidential unless an attacker can find a way to detect it.
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 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. Civil
liberties advocates fight this with the argument that “these limitations are an assault on
privacy”. This is where Steganography comes in. Steganography can be used to hide

9. 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. As mentioned earlier, neither Cryptography nor Steganography
are considered "turnkey solutions" to open systems privacy, but using both technologies
together can provide a very acceptable amount of privacy for anyone connecting to and
communicating over these systems.
CHAPTER 2

10. AN ADAPTIVE LSB-OPAP EMPLOYED PIXEL
DOMAIN STEGO TECHNIQUE (ALOS)
To enhance the embedding capacity of image steganography and provide
animperceptible stego-image for human vision, a novel adaptive number of leastsignificant
bits substitution method with private stego-key based on color imageranges are proposed in
this methodology. The new technique embeds binary bit streamin each 8 bit pixel value. The
methodalso verifies that whether the attacker has tried to modify the secret hidden (orstego-
image also) information in the stego-image. The technique embeds thehidden information in
the spatial domain of the cover image and uses simple (Ex-OR operation based) digital
signature using 140-bit key to verify the integrity fromthe stego-image. Besides, the
embedded confidential information can beextracted from stego-images without the assistance
of original images.
2.1 Proposed Methodology
The proposed scheme works on the spatial domain of the cover image and employed
an adaptivenumber of least significant bits substitution in pixels. Variable K-bits insertion
into least significantpart of the pixel gray value is dependent on the private stego-key K1.
Private stego-key consistsof four gray-level ranges that are selected randomly in the range 0-
255. The selected key showsthe four ranges of gray levels and each range substitute different
fixed number of bits into leastsignificant part of the 8-bit gray value of the pixels. After
making a decision of bits insertion into different ranges, Pixel p(x, y) gray value “g” that fall
within the range Ai-Bi is changed by embedding k-message bits of secret information into
new gray value “g’ ”. This new gray value “g’ ”of the pixel may go beyond the range Ai-Bi
that makes problem to extract the correct information at the receiver. Specific gray value
adjustmentmethod is used that make the new gray value “g’ ” fall within the range Ai-Bi.
Confidentiality isprovided by the private stego-key k1 and to provide integrity of the
embedded secret information,140-bit another key K2 is used. Digital signature of the secret
information with the key K2 wereobtained and appended with the information. The whole
message plus signature is embeddedinto the cover image that provides some bit overheads
but used to verify the integrity. At thereceiver key K1 is used to extract the message and key
K2 is used to verify the integrity of themessage.

11. 2.2 Private stego-key generation
Private stego-key K1 play an important role in proposed methodology to provide
security and deciding the adaptive K bits insertion into selected pixel. For a gray scale image
8-bit is used to represent intensity of pixel, so there are only 256 different gray values any
pixel may hold. Different pixels in image may hold different gray values. We may divide the
pixels of images into different groups based on gray ranges. Based on this assumption let four
ranges ofray levels are < A1-B1, A2-B2, A3-B3, A4-B4 > each range starting and ending
value are in8-bits.
2.3 Method to decide Bits insertion in each range
Let the four gray ranges decided by the stego-key are <A1-B1, A2-B2, A3-B3, A4-
B4> andnumber of pixel count from cover image in each range are < N1, N2, N3, N4 >.
Range withmaximum pixel count will hold maximum bits insertion let four bits, second
maximum count willhold three bits insertion and so on. In similar way we decide the bits
extraction from each range. ForExample assume key K1 is 0-64, 65-127, 128-191, 192-255
and let pixel count in eachrange from any image are 34,13238,17116, 35148. Then range first
insert one message bits in thepixel that comes within the range, range second insert two
message bits in the pixel,range thirdinsert three bit in the pixel ,range four insert four bits in
the pixel. In this manner we decide the bits insertion into eachrange.
2.4 LSB substitution
Least significant substitution is an attractive and simple method to embed secret
information intothe cover media and available several versions of it. We employ in propose
scheme adaptive LSBsubstitution method in which adaptive K-bits of secret message
aresubstituted into leastsignificant part of pixel value. Fig.2 shows entire method for K-bits
insertion.
g original value K- zero bits K- msg bits
Modify value g’

12. Fig 2: method for k- bits insertion
To decide arbitrary k-bits insertion into pixel, first we find the range of pixel value and then
findthe number of bits insertion decided by method given in section 2.3 and insert K-message
bitsinto least significant part of pixel using LSB. After embedding the message bits the
changed grayvalue g’ of pixel may go beyond the range.
CHAPTER 3
Pixelvalue in
8 - bits
AND OR
Value in 8 -
bits
K-
LSB’s

13. THE LSB BASED OPTIMUM PIXEL ADJUSTMENT
PROCESS (OPAP)
The Least significant substitution is a simple method to embed secret information into
the cover media. We employ in propose scheme adaptive LSBsubstitution method in which
adaptive K-bits of secret message are substituted into leastsignificant part of pixel value. To
decide arbitrary k-bits insertion into pixel, first we find the range of pixel value and then
findthe number of bits insertion decided by method given in section 2 and insert K-message
bitsinto least significant part of pixel using LSB.
Figure 3.1 shows the whole process.
Fig 3.1 LSB - OPAP
After embedding the message bits the changed grayvalue g’ of pixel may go beyond
the range. To make value within the range, reason is thatreceiver side required to count pixels
to extract message, pixel value adjusting method is appliedto make changed value within
range called as Optimum Pixel Adjustment Process.
After embedding the K-message bits into the pixel gray value g new gray vale g’ may
go outside the range. For example let our range based on key is 0-32. Let the gray value g of
the pixel is 00100000 in binary forms (32 in Decimal), decided K-bits insertion is 3-bits are
K = K+1

14. 111. The pixel new gray value g’ will be 00100111 in binary forms after inserting three bits
(39 in Decimal).
Modified value is outside the range. To make within the range 0-32, K+1 bits of g’ is
changed from 0 to 1 or via- versa and checked again to fall within range if not K+2 bit is
changed and so on until gray value fall within range. For example: 00100111- 00101111-
00111111- 00011111.

15. CHAPTER 4
IMPLEMENTATION OF ALOS: FLOW DIAGRAM
AND ALGORITHM
The algorithms used to implement Adaptive LSB-OPAP stego technique is described
as below:
4.1 Algorithms: Embedding
Input: Cover-image, secret message, keys K1, K2.
Output: Stego-image.
Step1: Read key K1 based on gray-Level ranges.
Step2: Read cover image
Step3: Decide No. of bits insertion into each range described in section 2.3
Step4: Read the secret message and Convert it into bit stream form.
Step5: Read the key K2.
Step6: Find the signature using K2 and append with the message bits.
Step7: For each Pixel
7.1: Find gray value g.
7.2: Decide the K-bits insertion based on gray ranges.
7.3: Find K-message bits and insert using method given in section 2.4
7.4: Decide and adjust new gray Value g’ using method described in Optimum pixel
adjustment process.
7.5: Go to step 7.
Step 8: end
The secret message is first converted into binary bit stream and its digital signature is
calculated using xor structure with the help of key-2 (140 bits), this signature is then
appended into the message and then embedding is done based on LSB substitution method by
key-1, on a cover image in spatial domain. The stego image is then transmitted through the
channel to the authorized receiver side, where the secret data embedded can be extracted
using the shared key.
Figure 4.1 and 4.2 shows the flow diagram for secret message embedding and
extraction along with digital signature respectively.

16. Fig 4.1 Message embedding with signature

17. 4.2 Algorithm: Extracting
Input: Stego-image, keys K1, K2;
Output: Secret information;
Step1: Read key K1 based on gray-level ranges.
Step2: Read the stego image.
Step3: Decide No. of bits extraction into each range described in section 2.3.
Step4: For each pixel, extract the K-bits and save into file.
Step5: Read the key K2 and find the signature of bit stream
Step6: Match the signature.
Step7: End
Fig 4.2 Message extractionand integrity check

18. CHAPTER 5
ADVANTAGES & APPLICATIONS OF PROPOSED
SYSTEM
5.1 Advantages
 High hiding capacity compared to LSB Substitution technique.
 Robust in nature, i.e., highly secure algorithm since two keys (key-1 and key-2) are
used.
 We get good quality of the stegoimage.
 High water marking level.
 Provides maximum possible payload.
 Embedded data is imperceptible to the observer.
5.2 Limitations
 High computational complexity.
 Requires a lot of overhead to hide a relatively bits of information.
This can be overcome by using HIGH SPEED COMPUTERS.
5.3 Applications
 In secret communication system.
 Military applications.
 Hiding and protecting of secret data in industry.
 Airlines.

19. CHAPTER 6
EXPERIMENTAL RESULTS AND DISCUSSIONS
In this implementation, Lena and baboon 256 × 256 × 3 colourdigital images have
been taken as coverimages and are tested for various ranges along with different size of secret
messages chosen. The effectiveness of thestego process has been studied by calculating
PSNR for the two digital images in RGB planesand tabulated. First analysis is used to select
the Range for embedding data (in this analysis Range1 is 0-64, 65-127, 128-191, 192-255)
and the results are tabulated in Table-12.3 for various Ranges. From the table we will
understand that Range2 for cover image baboon provides high Payload and Range1 for cover
image baboon provides low payload.
Range
Cover
image
Max bits that can be
embedded (payload)
No of bits embedded
Capacity
(bits/pixel)
PSNR
Range1
Lena 653149
51360 3.2016 44.9412
4768 3.141 55.5705
115360 3.2268 40.8688
Baboon 609524
51360 3.2927 44.7668
4768 3.2 54.8287
115360 3.0352 41.7503
Range2
Lena 693700
51360 3.624 43.494
4768 3.7338 53.4812
115360 3.6078 40.3313
Baboon 700087
51360 3.6488 43.2479
4768 3.7192 53.6941
115360 3.5019 40.2084
Table 6.1 Tabulated result for ALOS technique for secret image

20. Figure 6.1 Experimental result using Range1 for Baboon cover image
The above figure 6.1 shows the input cover image and output stego image and their
respective histograms. The above results are obtained for using Range1. The maximum
payload obtained is 609524 bits, on an average of 3.0352 bits per pixel with the PSNR of
41.7503.
The input cover image
0
200
400
600
The histogram of input cover image
0 100 200
The output stego image
0
200
400
600
800
The histogram of stego image
0 100 200

21. Figure 6.2 Experimental result using Range2 for Lena cover image
The above figure 6.2 shows the input cover image and output stego image and their
respective histograms. The above results are obtained for using Range2. The maximum
payload obtained is 31975 bits, on an average of 3.6078 bits per pixel with the PSNR of
40.3313.
The input cover image
0
200
400
600
800
The histogram of input cover image
0 100 200
The output stego image
0
500
1000
The histogram of stego image
0 100 200

22. CONCLUSION
This novel image steganographic model results in high-capacity embedding/extracting
characteristic based on the Variable-Size LSB substitution. In the embedding part based on
stego-key selected from the gray value range 0-255, it uses pixel value adjusting method to
minimize the embedding error and adaptive 1-4 bits to embed in the pixel to maximize
average capacity per pixel. Using the proposed method, it can be shown that atleastfour
message bits in each pixel can be emebbed, while maintaining the imperceptibility. For the
security requirement, two different ways are proposed to deal with the issue. The major
benefit of supporting these two ways is that the sender can use different stego-keys in
different sessions to increase difficultly of steganalysis on these stego images. Using only the
stego-keys, which is used to count the number of pixel in each range and second 140-bit key
to verify the integrity of the message, the receiver can extract the embedded messages
exactly. Experimental resultsverify that the proposed model is effective and efficient.
REFERENCES

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