This document summarizes a student project on blind recovery of hidden data from digital carriers. The proposed system uses both cryptography and data hiding techniques for security. It embeds secret data into digital images using least significant bit replacement and extracts the hidden data using a single static algorithm called MIGLS. Experimental results show the encrypted message can be accurately recovered from stego images. The system provides confidential communication and data security with applications in access control, military use, and protected data distribution.

1. A Project On
“BLIND RECOVERY OF
COVERED DATA FROM DIGITAL
CARRIER ”
Presented By-
NIKAM AJINKYA
HARBA VIJAY
AVHAD SOMINATH
GUIDED BY-
Prof. M.N KALE

2.  Introduction
 Existing System
 Proposed System
 Encryption Techniques
 Decryption Technique
 Results
 Advantages
 Conclusion
 Reference
Classification

3.  Digital Data embedding.
 Applications may vary.
 Improved Embedding performance.
 Improved recovery performance.
 Extension to limited attacking.
 After extracting the data hidden original image is retrieved.
Classification
What Is Data Hiding ?

4.  Reversible Data Hiding technique image is compressed
and encrypted
 Secret encryption key used can access the image and
decrypt
 After extracting the data hidden original image is
retrieved.
Classification

5. Classification
Compressed and Encrypted use in Existing
System

6.  Reduce the risk of using Cryptography algorithm alone.
 Cryptography algorithm + Information hiding technique.
 Symmetric key Encryption.
 Attention on the blind recovery of secret data hidden.
 Encryption algorithm remain hidden from host.
Classification

7.  Vessel data.
 Steganography is that embedding capacity is very large.
 For a 'normal' image, roughly 50% of the data might be
replaceable with secret data.
 Password use as a symmetric key.
Classification

8.  Image Encryption.
 Size of image The host image is an 8-bit or higher
grey level image
 For color host images, the binary cipher text can be
inserted into one or all of the RGB components.

9.  Image Decryption.
 For color images, the data is decomposed into each RGB
component
 Each 1-bit layer is extracted and correlated with the
appropriate cipher.
 into an 8-bit image based on floating point numbers.

10. SYSTEM ARCHITECTURE

11.  Random Distribution of Message
 Generation of Seed
 Random position Selection
 Bit Smarter

12. Input : Message,shared secret,cover image,seed.
Output : Stego image.
initialize with shared seed
while data left to embed do
get pixel from cover image
if pixel !=0 and pixel !=1 then
get next LSB from message
replace LSB with Message LSB
end if
insert pixel into stego image
end while

13. Color Base
• There is a large amount of redundant bits in
an image. The redundant bits of an object are
those bits that can be altered.
• The alteration cannot be visibly detected by
human eyes, due to use of blue color.
• Every pixel in a color image composed of
three colors Red, Green and Blue so every
pixel contains 24 bits

14. Classification
LSB
• Replaces the least significant bit of cover image
with the message bits..
• Image alteration is not perceptible for any human
eyes
as its value will affect the pixel value only by “1”.
• So, this property is used to hide the data in the
image. Here we have considered last two bits as LSB
bits as they will affect the pixel value only by “3”.

15.  Divide the cover image into set of groups (2x
8)pixel.
 Separate the R, G and B band of each pixel.
 Extract hash value.
 Random number generation
 Use hash values to determine the selected
pixel for each block.
 Data bits are replaced on selected pixels
Classification

16. M-IGLS
1. First Convert Host Image To Observation Vector Form
y(m), m=1,2…...m
2. Create Partition
N1.N2/M
3. Number Of Carrier Used By Embedder.
4. Finding Noisy region.
5. Create M-IGLS Header
6. Stego Analysis.

17. •H - Host image
•M - finite image alphabet.
•N1,N2 – image size in pixel.
Image

18. Algorithm

19. Results
Encoding Process

20. Classification
Images

21. Classification
Decoding Process

22. Classification
Decoded Message

23. Classification
Advantages
1) Confidential communication and secret data
storing
2) Protection of data alteration
3)Access control system for digital content
distribution
4) Media Database systems

24. Classification
Applications
1)Confidential communication and secret data
storing
2)Protection of data alteration
3)Access control system for digital content
distribution
4)Use in Military application to Protect data.
5)Use To Protect data In School , Colleges ,
Office

25. Classification
Conclusion
For Experimental studies we have taken four different
algorithms for encryption Hide And Seek, LFSR, Single Bit
LSB, Color Based. and we decrypted the Message from our
Single Static Algorithm MIGLS. with Attention on the blind
recovery of secret data hidden in medium.
Hence in this project in decryption phase algo decrypt
the Message from stego image by using Single Static
Algorithm MIGLS successfully.

26. Classification
Reference
1] Extracting Spread-Spectrum Hidden Data from Digital Media Ming Li, Member,
IEEE, Michel Kulhandjian, Dimitris A. Pados, Member, IEEE, Stella N. Batalama,
Senior Member, IEEE, and Michael J. Medley, Senior Member IEEE,IEEE
Transactions on Secure Computing vol:8 NO:7 2013.
2] Literature Survey On Modern Image Steganographic Techniques Priya Thomas
Department of Computer Science and Engineering Nehru College of Engineering
and Research Center, Kerala, India.International Journal of Engineering Research
Technol-ogy Issue 5, May - 2013 ISSN: 2278-0181.
3] M. Li, D. A. Pados, S. N. Batalama, and M. J. Medley, ”Passive spreadspectrum
steganalysis,” in Proc. IEEE Intern. Conf. Image Proc. (ICIP), Brussels,
Belgium,Sept. 2011, pp. 1997-2000.
4] A. Valizadeh and Z. J. Wang, ”Correlation-and-bit-aware spread spectrum
embedding for data hiding,” IEEE Trans. Inform. Forensics and Security, vol. 6 , pp.
267-282,June 2011.