Steganography in images

STEGANOGRAPHY IN
IMAGES
Presented by
Aishwarya Korde.
Roll no.: 322026
Guided by
Amit Barve Sir

 INTRODUCTION
 MOTIVATION
 TYPES OF STEGANOGRAPHY
 IMAGE STEGANOGRAPHY
 LSB TECHNIQUE
 GRAY SCALE COVER IMAGES
 EFFICIENCY ADAPTIVE
STEGANOGRAPHY
 DATA HIDING IN FLASH MEMORY
 CONCLUSION AND FUTURE SCOPE
 REFERENCES

Steganography is defined as the study of invisible
communication. Steganography usually deals with the
ways of hiding the existence of the communicated data
in such a way that it remains confidential.
Steganography is a Greek word which means
concealed writing. The word “steganos” means
“covered “ and “graphial “ means “writing” . Thus,
steganography is not only the art of hiding data but
also hiding the fact of transmission of secret data.
Steganography hides the secret data in another file in
such a way that only the recipient knows the existence
of message. In order to safely transmission of
confidential data, the multimedia object like audio,
video, images are used as a cover sources to hide the
data .

1)The internet allows for easy dissemination of information
over large areas.
2)This is both a blessing and a curse since friends all over the
world can view your information but so can everyone else.
Encrypting data has been the most popular approach to
protecting information but this protection can be broken
with enough computational power.
3) An alternate approach to encrypting data would be to hide it
by making this information look like something else. This
way only friends would realize its true content.
4)In particular, if the important data is hidden inside of an
image then everyone but your friends would view it as a
picture. At the same time your friends could still retrieve the
true information. This technique is often called data hiding
or steganography

 IMAGE
 AUDIO
 VIDEO
 TEXT
 NETWORK

Digital Medium to Achieve Steganography

 Image steganography is method of information hiding into
cover-image and generates a stego-image.
 This stego-image then sent to the other party by known
medium, where the third party does not know that this
stego-image has hidden message.
 After receiving stego-image hidden message can simply be
extracted with or without stego-key (depending on
embedding algorithm) by the receiving end .
 Without stego-key, where embedding algorithm required a
cover image with message for embedding procedure.
 Output of embedding algorithm is a stego-image which
simply sent to extracting algorithm, where extracted
algorithm unhides the message from stego-image.

 Terminologies
1) Cover-Image: Original image which is used as
a carrier for hidden information.
2) Message: Actual information which is used to
hide into images. Message could be a plain text
or some other image.
3)Stego-Image: After embedding message into
cover image is known as stego-image.
4) Stego-Key: A key is used for embedding or
extracting the messages from cover-images and
stego-images.

 Spatial Domain Methods
 Transform Domain Technique
 Distortion Techniques
 Masking and Filtering

 It is a type of spatial domain methods.
 This method is most commonly used for hiding
data. In this method the embedding is done by
replacing the least significant bits of image pixels
with the bits of secret data.
 The image obtained after embedding is almost
similar to original image because the change in the
LSB of image pixel does not bring too much
differences in the image.
 In the LSB matching, the choice of whether to add
or subtract one from the cover image pixel is
random.

Example:
00100111 11101001 11001000
00100111 11001000 11101001
11001000 00100111 11101011
00100111 11101000 11001000
00100110 11001000 11101000
11001001 00100111 11101011

 The message embedding is performed for the two
cover image pixels at a time. The gray-level values
of those two pixels are x(i) and x(i+1).
 After the message embedding, the value of the ith
message bit m(i) is equal to the LSB of stego
image’s ith pixel y(i) . The value of the (i+1)th
message bit m(i+1) is a function of y(i) and y(i+1).
 This method allows a selection of
addition/subtraction of to carry information,
because the selection can set a binary
function(y(i),y(i+1)) to the desired value.

 If a binary function has the following property
f(l-1,n) f(l+1,n) ….1
 If a binary function f(l,n) is of the form
f(l,n) f(l,n+1) ….2
 Then both an increase and a decrease of by n
one will change the value of the function f(l,n).

For pair of pixels
input: a pair of cover image pixels x(i),x(i+1)
message bits: m(i),m(i+1)
Output: a pair of stego image pixels y(i) ,y(i+1)
If m(i)=LSB(x(i))
if m(i+1) f(x(i),x(i+1))
y(i+1) = x(i+1) +1
OR
y(i+1) = x(i+1) -1
else
y(i+1) = x(i+1)
end

y(i) = x(i)
else
if m(i+1) = f(x(i)-1,x(i+1))
y(i)=x(i)-1
else
y(i)=x(i)+1
end
y(i+1) = x(i+1)
end

1)Data Embedding Algorithm
STEP 1: Separate RGB component
STEP 2: Choose the component
STEP 3: Parameter Initialization
STEP 4: Preprocess
STEP 5:The resulting image is rearranged as a row vector V.
These vector is divided into nonoverlapping embedding units
with every two consecutive pixels, (xi ,xi+1)
where i=1,3..., m, n-1 assuming ‘n’ is an even number.
STEP 6: Encryption
STEP 7: Region Selection
EU (t) = {(xi ,xi+1) || xi - xi+1 | ≥ t,
for all (xi ,xi+1) € V }

STEP 8: Capacity Estimation
T=max {2 x | EU (t)| ≥ | E(M) | }
STEP 9: Data Hiding
Case 1:
LSB (xi) = mi & LSB (f (xi, xi+1) = mi+1
then,
(xi’ ,xi+1’) = (xi, xi+1)
Case 2:
LSB (xi) =mi & LSB (f (xi, xi+1)) ≠ mi+1 then
(xi’ ,xi+1’) = (xi, xi+1+ r). where r = ±1.
Case 3:
LSB (xi) ≠ mi & LSB (f (xi-1, xi+1)) = mi+1
then
(xi’ ,xi+1’) = (xi-1, xi+1)
Case 4:
LSB (xi) ≠ mi & LSB (f (xi-1, xi+1)) ≠ mi+1
then
(xi’ ,xi+1’) = (xi+1, xi+1)
where mi and mi+1 denote two secret bits to be embedded.

The function ‘f’ is defined as f(a,b) = (a/2)+b. r is a
random value in{-1,+1} and ( xi’ , xi+1’)denotes the
pixel pair after data hiding. After the above
modifications xi’ and xi+1’ may be out of [0,255],
or the new difference | xi’ - xi+1’| may be less
than the threshold T. In such cases, we need to
readjust them as (x’’i , x’’i+1) by
x'’i = xi’ +4k1
x’’i+1 = x’i+1 + 2k2
k1 and k2 posses the value of either 0 or 1.
we have
LSB (xi ‘’) = mi & LSB (f (x’’i , x’’i+1) = mi+1
STEP 10: Post process
STEP 11: Combine RGB.
STEP 12: Preset Region

Data Extraction Algorithm
STEP 1: Separate RGB component
STEP 2: Parameter Extraction
STEP 3: Preprocess
STEP 4: Region Identification
STEP 5: Data Extraction
STEP 6: Conversion of bits into ascii value

Part A – Composing the
message
1) For each selected page in a block
2) Generate the group for each
message bit via the page hiding
key
3) Assign each group 0 or 1
according to the embedded data
4) For each bit
5) If its group will represent a
message ”1”
6) Set it to be programmed 0
7) Else
8) Set it to be programmed 1
9) End if
10 )End for
11) End for
Part B – Writing the message to
Flash
1) For each selected block
2) For i = 1, 2, ..,N (N is the number of
Hiding PE cycles)
3) Erase the block
4) Program every selected page
5) End for
6) End for

 Performance of edge adaptive steganography for color
image with increasing values of parameters is analyzed. In
addition to that, color image is separated into RGB layers
and then data hiding is performed. The image quality after
data embedding is very important for better performance of
steganography methods.
 When threshold is high only minimum set of characters can
be embedded. As the threshold value decreases more
characters can be implanted.
 Separation of image into RGB component results increases
the embedding capacity.
 This method encrypts the message, which improves the
security
 This scheme can be applied to other covers such as audio
and video which is taken as the future work.

 Weiqi Luo, fangjun Huang and Jiwu Huang, “Edge
Adaptive Image Steganography Based on LSB
Matching Revisited”, IEEE transaction on Information
forensics and security, Vol.5, No. 2, pp. 201-214, 2010.
 J. Mielikainen, “LSB matching revisited,” IEEE Signal
Process. Lett.,vol. 13, no. 5, pp. 285–287
 X. Zhang and S. Wang, “Steganography using
multiple-base notational
 system and human vision sensitivity,” IEEE Signal
Process. Lett., vol.12, no. 1, pp. 67–70, Jan. 2005.
 A. Ker, et al “Improved detection of LSB
steganography in grayscale images,” in Proc.
Information Hiding Workshop, vol. 3200, Springer LNCS,
2004, pp. 97–115.

Steganography in images

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