SEMINAR

REPORT ON

Reversible

Data Hiding in Encrypted Images

by

Reversible Image Transformation

SUBMITTED

TO

Department of Computer Science &

Engineering

by

Chethan

R Bhat

Registration number: 170948002

II

Sem M. Tech (CSIS)

Name

& Signature of guide

Mr.

Kishore B

Assistant Professor – Selection Grade

Evaluator 1 Evaluator

2

Dr. Manjunath K N Mr. Rohit Verma

Assistant Professor-Senior

Scale Assistant Professor

CHAPTER 1

INTRODUCTION

Information

processing in the encrypted domain has attracted considerable research

interests. In numerous applications, for example, distributed computing and

assigned estimation, the content owner may need to transmit data to a remote

server for processing further if required. In few cases, the content proprietor

may not confide in the administration provider and requirements to encode the

information before transferring. Accordingly, the service supplier must have

the capacity to do the handling in the encrypted space. Scarcely any works have

been finished for information processing in an encryption space, for instance,

compressing of encoded pictures, including a watermark into the encrypted

picture, and reversibly hiding of information into the encrypted picture. Not

at all like powerful watermarking, reversible data hiding (RDH) is able to obtain

complete image reproduction and information extraction.

Reversible data

hiding (RDH) in pictures is the technique, in which the first cover picture may

be losslessly recuperated before which the hidden text has been extracted. Since

this imperative strategy is utilized as a part of medical domain, military area

and law crime scene investigation, where no loss of the cover image is

acknowledged. Since it keeps up the superb property that the cover image can be

recouped after inserted information is removed while securing the picture

substance’s privacy.

Reversible

Data Hiding (RDH) of encoded pictures has the great property that the first

cover picture can be losslessly reestablished after the implanted information

is removed while securing the picture substance’s privacy. This procedure is

helpful in applications, for example, restorative imaging where the first

picture must not get adjusted after the implanted information are separated.

Basically, RDH is acknowledged by the utilization of repetition in the first

picture. The two vital prerequisites for RDH systems: deformity to be low and

the embedding capacity to be huge.

CHAPTER 2

LITERATURE

SURVEY

2.1 A

reversible data hiding method for encrypted images.

W. Puech, M.

Chaumont and O. Strauss 1 first proposed in which the proprietor encodes the

first picture by Advanced Encryption Standard, and the information hider

installs one bit in each of the block containing pixels, implying that the

inserting rate is bits per pixel. On the recipient side, the information

extraction and picture reconstruction are acknowledged by investigating the

standard deviation amid decryption of the marked encrypted picture. This

technique requires that picture decoding must be done alongside information

extraction operations. At the end of the day, decoding and extraction are

indistinguishable.

2.2 Lossless

and Reversible Data Hiding in Encrypted Images with Public Key Cryptography.

Xinpeng Zhang,

Jing Long, Zichi Wang, and Hang Cheng 2 proposed a lossless and reversible

information concealing method for public key-encoded pictures with

probabilistic and homomorphic properties of cryptosystems. With such plans, the

pixel division and rearrangement is evaded and the encryption/decryption

operation is performed on the cover pixels specifically to such an extent that

the measure of scrambled information and the computational intricacy are

brought down. Because of information implanting on encoded space may bring

about less deformity in plaintext area due to the homomorphic property, the

inserted information can be separated trailed by the recuperation of actual

content from the unscrambled picture. With such joined strategy, the

beneficiary can remove some portion of implanted information earlier

unscrambling, and concentrate another piece of embedded information and recover

the actual plaintext picture after decoding.

2.3 Reversible

data hiding in encrypted images by reserving room before encryption.

K. Mama, W. Zhang,

X. Zhao, N. Yu, F. Li, 3 proposed a novel strategy for RDH in encoded

pictures, for such technique they don’t “vacate room after

encryption” and “reserve room before encryption” with

conventional RDH calculation, and along these lines it is very simple for the

information hider to install information in a reversible way into the scrambled

picture. The proposed technique can accomplish the reversibility that is

information extraction and picture recuperations are conceivable with no

mistake. As a matter of first importance they exhaust out the room with inserting

least significant bits of a few pixels into different pixels with a

conventional RDH strategy; afterward scrambled the picture, so that the places

of these least significant bits in the encoded picture may be utilized later to

shroud the information. The proposed technique not just accomplishes a

different information extraction from picture unscrambling yet additionally

accomplishes magnificent performance.

2.4 An improved reversible data hiding in encrypted

images using side match.

W. Hong 4 proposed a superior form of reversible

information concealing strategy in encoded pictures, which partitions the

scrambled picture into blocks, and each of the block conveys one bit by

flipping three least significant bits of an arrangement of pre-characterized

pixels. The information extraction and picture recuperation can be gotten by

checking the piece smoothness. Information recuperation of piece is performed

in plunging request of the outright smoothness distinction between two

competitor blocks. To additionally decrease the error rate the side match

procedure is conveyed.

2.5 Secret-fragment-visible mosaic image–a new

computer art and its application to Information hiding.

I. – J. Lai and W. – H. Tsai 5 proposed a picture

transformation technique, which chooses an objective picture like the secret

picture, at that point replaces each of the blocks in the objective picture by

a quite similar block of the secret picture and implants the map amongst secret

and target blocks; it shapes an encoded picture of the secret picture. To

locate the most similar block a greedy search strategy is utilized. This

strategy is reasonable for an objective picture comparative with the secret

picture, and the visual nature of encoded picture isn’t all that great in spite

of the fact that it is reversible.

2.6 A Lossless Data Hiding Method by Histogram

Shifting Based on an Adaptive Block Division Scheme.

Che-Wei Lee and Wen-Hsiang Tsai1 6 proposed lossless

information concealing strategy on the basis of histogram shifting technique.

This strategy utilizes a plan of adaptive division of cover pictures. The cover

pictures are separated into the blocks to yield expansive information

concealing limits. Picture qualities has been progressed. The technique is to

break a bottleneck of information concealing rate expanding at the picture. The

picture square size of 8×8 size. Such procedures are found in existing

histogram moving strategies. Four methods for block divisions are planned. The

one which gives the biggest information concealing limit is adaptively chosen.

2.7 High Capacity Lossless Data Embedding Technique

for Palette Images Based on Histogram Analysis.

N.

A. Saleh, H. N. Boghdad, S. I. Shaheen, A. M.Darwish 7 proposed, Data

hiding in encrypted image as one of the way of transmission of data securely

but due to technical aspects after the data extraction there may be large loss

in the image quality as well as original content of image. In the beginning

there are problem regarding the capacity of carrying data but recently this

problem is overcome. There is a possibility of huge loss in original quality of

image but it can carry high capacity data. This would mean that we are facing

only the problem of original content of image. To obtain solution for this problem,

the focusing area is Histogram. Histogram is nothing but graph of image which

is drawn on x-axis and y axis with references to the pixel values. Histogram analysis

indicates that we plot the graph of original image to avoid the loss of quality

of image after the issues with decryption.

CHAPTER 3

METHODOLOGY

3.1. Block Pairing

To influence the changed picture J’ to look like target picture J,

after change, each changed piece will be having the nearest mean and standard

deviation (SD) with the objective square. So we initially process the mean

value and standard deviation of each square of I and J individually. Give a

square B a possibility to be an arrangement of pixels with the end goal that B =

{p1, p2,. . . . pn}, and afterward the mean and standard deviation of this

square is computed as takes after:

While coordinating pieces between unique picture and target

picture, we match obstructs with nearest standard deviations. To recuperate the

first picture from the changed picture that is transformed image, the places of

the first squares ought to be recorded and inserted into the changed picture

with a RDH technique. We initially group the squares as indicated by their standard

deviation esteems before blending them up. Actually, we found that the standard

deviation estimations of most squares pack in a little extend near zero and the

recurrence rapidly falls down, along with the expansion of the standard

deviation esteem. This way, we are able to separate the pieces into classes of two

with unequal extents: class 0 for hinders with littler standard deviations, and

class1 for obstructs with bigger standard deviations, and match up the squares

having a place with a similar class. By appointing the greater part of pieces

to the class 0 we can keep away from the vast deviation of standard deviations

between a couple of squares and proficiently pack the lists in the meantime. In

this paper, we propose to isolate both the first and target pictures into

non-covering 4 X 4 pieces and compute the standard deviations of each square.

We first gap the squares of unique picture I into 2 classes as indicated by the

quantile of standard deviations and relegate a class mark, 0 or 1, to each

piece. We filter the objective picture and mark first n 0 hinders with the

littlest standard deviations as Class 0, and the rest n 1 obstructs as Class 1.

A

basic case on the proposed piece blending technique is appeared in the Figure 1,

within which the picture just comprises of ten squares. By setting ? = 70, we

relegate 7 obstructs with littlest SDs into class 0, and the rest 3 hinders

into class 1 in the first picture. In the objective picture, in spite of the

fact that the eighth and ninth piece have a similar SD esteem 5, the eighth

square is appointed to class 0 yet the ninth square is allocated to the class

1, since class 0 can just incorporate 7 blocks as dictated by the class 0 of

the first picture. Subsequent to naming the class lists, we get a class index

table (CIT) for unique picture and target picture individually, which will be

useful for understanding the method of piece blending. As indicated by the

blending standard, the main square of the first picture is combined up with the

forward piece of the objective picture, in light of the fact that the two is the

principal square of class 1 as appeared in the class index table; the second

square of unique picture is matched up with the ninth piece of target picture,

on the grounds that two is the second square of class 1, etc. The blending

result is recorded in Figure 2, which can be created by the class index table

of unique picture and the class index table of the objective picture.

For

each combine of squares (B, T), the first piece B will be changed to target

piece T by mean moving and square turn, yielding T’. By supplanting every T

with T’ in the objective picture, the sender will produce the changed picture.

Note that the two operations of mean moving and square revolution won’t change

the standard deviation esteem, so T’ has an indistinguishable standard

deviation from B. In this way, the standard deviations in changed picture is

just a stage of those in unique picture. Along these lines, to re-establish the

first picture from the changed picture, the recipient just has to know the standard

deviation of the first

picture.

Fig.

1 Example of Block Pairing a) Original Image b) Target Image c) Transformed Image

Fig.

2 Block pairing result of the example Fig. 1

3.2. Block Transformation

By

the piece matching technique depicted above, in each combine (B, T), the two

squares have close standard deviation esteems. Hence, while changing B towards

T, we just need a mean moving change that is reversible.

Let

the first square B ={p1 , p2 , . . . , pn }, and the relating target piece T

={p’1 , p’2 , . . ., p’n }. With Eq. (1), we ascertain the methods for B and T

and mean them by uB and uT separately. The changed piece T’= { p”1 , p”2 , .

. . , p”n} is created by the mean moving as takes after:

where (uT ? uB ) is the distinction between the methods for target

square and unique piece. We need to move every pixel estimation of unique piece

by adequacy (uT ? uB) and in this way the changed square has a similar mean

with the relating target piece. Notwithstanding, in light of the fact that the

pixel esteem p”i ought to be a number, to keep the change reversible, we round

the distinction to be the nearest whole number as

?u = round(u T ? u B ) (4)

and move the pixel esteem by ?u, in particular, each p”i is

gotten by

Note

that the pixel esteem p”i ought to be a whole number in the vicinity of 0 and

255, so the change (5) may bring about some finished stream/undercurrent pixel

esteems. To maintain a strategic distance from such changed squares went

without by (5), we accept that the most extreme flood pixel esteem is OVmax for

?u ? 0 or the base undercurrent pixel esteem is UNmin for ?u