International Conference on Explorations and Innovations in Engineering & Technology (ICEIET – 2016)

Adaptive Steganalysis of Least Significant Bit

in wrl images Ms.Mohana Priya.S Ms.Bindiya.S

UG Scholar, Department of Information Technology, UG Scholar, Department of Information Technology, SNS College of Engineering, SNS College of Engineering,

Coimbatore, India Coimbatore, India mohana995@gmail.com bindiyasiv@gmail.com

Ms. Aruna.A

Assistant Professor, Department of Information Technology, SNS College of Engineering,

Coimbatore, India aruna1804@gmail.com

ABSTRACT

Steganography is the art of covert communication. A stenographic system thus embeds

hidden content in unremarkable cover media. In earlier systems, reversible data hiding algorithm is

used. The drawbacks behind this algorithm are the

hiding rate (1.536 bpp) often causes more distortion in the image content and low degradation in the image

quality. This system was proposed to improve the

security and robustness done in 3D images. Initially, the 3D image is viewed and the secret message is

embedded into the 3D image. The secret message is hidden in the 3D image using the key file. After the

embedding process, the 3D image is sent to the

receiver. The receiver receives the secret key and extracts the message embedded in the 3D image. The

secret data or secret message is viewed by the receiver.

If the secret key entered by the receiver is wrong then only the 3D image is shown to the receiver. The

accuracy rate used in this system is 97.6%. Here, the

3D model which is based on least significant bit algorithm to protect the embedded information. This

algorithm can be widely used for digital protection and other aspects of identity hidden.

Keywords – Least significant bit algorithm; 3D

images; digital watermarking; data confidentiality;

Hidden data; Secret key; Data extraction.

I INTRODUCTION

Data hiding technique aims to embed some secret information into a carrier signal by altering the

insignificant components for copyright protection or

covert communication. During the past years, a

steganography technique has received more attention.

Steganography is the art and science of invisible communication, which attempts to conceal the existence

of hidden messages. The steganalysis, on the other

hand, aims to discover the hidden data from the cover medium.In the recent years, 2D and 3D images are the

most popular digital media for carrying secret messages. JPEG format for 2D images and VRML

format for 3D images are the most popular image

formats for image storage and exchange on the Internet at this time, many information hiding methods and/or

tools implement hiding message in 3D images.

With the development of science and technology,

three dimensional models have been used in many fields, such as medical industries, movies, video games,

constructions and so on. With the growth of application

areas, more and more digital products of three-dimensional model spread on the network. The

copyright protection of three-dimensional model has become increasingly important. Study on the three

dimensional model for digital watermarking technology

is becoming a new field of digital watermarking research [1]. Like the two-dimensional image

watermarking, digital watermarking algorithm for three-

dimensional model began from the spatial algorithm.

In 2014, X.Feng proposed two different

algorithms to embed watermark into the three-dimensional model [5], but the watermark information

is not encrypted. Once the watermarking algorithm is

cracked, the watermark information is not safe. In the same year, Wang put forward a digital watermarking

algorithm for three-dimensional model which is based on the structural feature of the vertex distribution [6]:

the algorithm has the drawbacks that the amount of data

embedded is limited. In general cases, the data-hiding operation will result in distortion in the host signal.

However, such distortion, no matter how small it is, is

unacceptable to some applications, e.g., military or medical images.

In this case it is imperative to embed the additional

secret message with a reversible manner so that the original contents can be perfectly restored after

extraction of the hidden data. A number of reversible

data hiding techniques have been proposed, and they can be roughly classified into three types: lossless

compression based methods, difference expansion (DE)

methods, and histogram modification (HM) methods. The lossless compression based methods make use of

statistical redundancy of the hos t media by performing lossless compression in order to create a spare space to

accommodate additional secret data. Thereby, this paper

applies encrypted holographic watermarking algorithm to the three-dimensional model to protect the watermark

information (such as copyright information with

specific identity) and improve the security, capacity of watermark embedding. We can get the original

watermark information from the encrypted watermark

only by the decrypt template and check the information of specific authentication. The proposed algorithm has

high security and good robustness to common attacks.

II EXISTING SYSTEM

EMBEDDING ALGORITHMS

The flow chart of watermark embedding algorithm is shown in Figure 1.The steps are as follows:

1) Process the three-dimensional model with an affine

invariant; 2) Sort i of vertices in ascending order, and store the corresponding r in the matrix j D with sizen n

, then keep the location information of i in a mark

matrix. j is a positive integer and satisfies j ( ) 1 N the empty in the last matrix can be filled with zero; 3)

Generate random matrix a and matrix b as double random phase modulation, which is key to encryption

and decryption of holographic watermark; 4) The

watermark image (such as copyright information)

modulated by the random phase a takes Fourier

transform, then the transformed image is modulated by the random phase b and takes inverse Fourier transform;

5) Coaxial holographic watermark H is then constructed

by double random phase encryption; 6) Three-dimensional Models is reconstructed by matrix. 8)

Spherical coordinates (r, θ, φ) is converted into

Euclidean coordinates (x, y, z), and the formula of

computation.(11) The hologram algorithm is used

successfully in the 3D model watermarking.

EXTRACTION ALGORITHMS

The flow chart of watermark extraction algorithm is shown in Figure 2.The steps are as follows:

1) Relocate [9] and Resample operator [10]; 2) Affine

invariant processing of three-dimensional model; 3) Move the barycentre of the model to the coordinate

origin and Euclidean coordinates (x, y, z) is converted into spherical coordinates (r, θ, φ); 4) Sort i of vertices

in ascending order, and find a matrix with size n

according to the location of i in mark matrix. If we cannot find the full matrix, then choose the matrix

which is relatively integrate and the missing part can be

filled with zeros.

In this way, hologram watermarking matrix 'h is reconstructed (With multiple watermarking

embedded, the relatively integrity one is enough); 5) Coaxial holographic watermark H is given 6) the

holographic watermark H takes Fourier transform. And

the transformed information is modulated by the random phase –b and takes inverse Fourier transform.

Then it is modulated by the random phase -a. And we

get the watermark image.

III PROPOSED SYSTEM

Steganography, the art of covert communication. It hides the presence of a message. It can imperceptibly

embed data into a cover object. Traces of data embedding can be found within the characteristics of

the stegano objects.The dissemination such as using the

(VRML) to represent 3D graphics on the Web.3D models have become potential covers for covert

communication. Data Hiding is the art of hiding the fact

that communication is taking place, by hiding information in other information.VRML File formats

can be used(.wrl). 3D images are the most popular because of their frequency on the Internet. Data is

encrypted domain preserves the confidentiality of the

content. LSBs of the elements pointed by the determined locations are used for embedding and

extraction. Embedded watermark data and Spread-

spectrum based watermarking technique is proposed. It

avoids data degradation due to traditional watermarking.

ALGORITHM:

The procedure of the proposed algorithm is illustrated

in Fig. 1. Given that totally pairs of histogram bins are to be split for data embedding, the embedding

procedure includes the following steps: 1) Pre-process:

The pixels in the range of and are processed as mentioned in SectionII-C excluding the first 16 pixels in

the bottom row. A location map is generated to record

the locations of those pixels and compressed by the JBIG2 standard [11] to reduce its length. 2) The image

histogram is calculated without counting the first 16

pixels in the bottom row. 3) Embedding: The two peaks (i.e. the highest two bins) in the histogram are split for

data embedding by applying Eq. (1) to every pixel counted in the histogram. Then the two peaks in the

modified histogram are chosen to be split, and soon

until pairs are split. The bit stream of the compressed location map is embedded before the message bits

(binary values).The value of, the length of the

compressed location map, the LSBs collected from the 16 excluded pixels, and the previous peak values are

embedded with the last two peaks to be split. 4) The lastly split peak values are used to replace the LSBs of

the 16 excluded pixels to form the marked image.

The extraction and recovery process include the

following steps: 1) The LSBs of the 16 excluded pixels are retrieved so that the values of the last two split

peaks are known. 2) The data embedded with the last

two split peaks are extracted by using Eq. (2) so that the value of, the length of the compressed location map, the

original LSBs of 16 excluded pixels, and the previously split peak values are known. Then the recovery

operations are carried out by processing all pixels

except the 16 excluded ones with Eq.(3).The process of extraction and recovery is repeated until all of the split

peaks are restored and the data embedded with them are

extracted. 3) The compressed location map is obtained from the extracted binary values and decompressed to

the original size. 4) With the decompressed map, those

pixels modified in pre-process are identified.

Among them, a pixel value is subtracted by if it is less than 128, or increased by otherwise. To comply

with this rule, the maximum value of is 64 to avoid ambiguity. At last, the original image is recovered by

writing back the original LSBs of 16 excluded pixels.

SYSTEM ARCHITECTURE:

MODULES:

• Viewing 3D-image module

• Data hiding module

• Network process module

• Data extracting module

VIEWING 3D-IMAGE MODULE

In this Module only we are going to view the 3d objects. A steganographic method called adjacent bin

mapping (ABM) is presented. Firstly, it is applied to 3D

geometries by mapping the coordinates within two adjacent bins for data embedding. When applied to

digital images, it becomes a kind of LSB hiding, namely

the algorithm. In order to prevent the detection using a metric named histogram tail, the hiding is performed in

a pseudorandom order.

DATA HIDING MODULE

We are going to apply Robust Watermark Embedded scheme is used in it.After apply the watermark

technique the data will hided.The hided data we will

send through network and generate one secret key for every data. Finally the watermarked data and the key

will send to destination through network.

NETWORK PROCESS MODULE

Then upload the two files we give the data which we are

going to hide in the three dimensional file. Then we will do the parity check by taking the last bit. Here, the LSB

algorithm is used.

DATA EXTRACTING MODULE

In this module we are going to retrieve the hided data from the 3d model. This process will be done by given

the saved 3d file in hiding module. After checking the

source file it wIll retrieve the content from the given file. A robust watermarking algorithm is proposed to

embed watermark into compressed and encrypted.

IV EXPECTED RESULTS

The expected results of the proposed system are the

measured in terms of efficiency and performance rate. The proposed work has the efficiency rate of 97.6%.The

performance rate will be high when compared to the existing system. The secret message sent in the 3D

images will be maintained with high security. The

message sent through 3D images will achieve high confidentiality. The encryption and decryption done in

the proposed system will be quite easy when compared

to the existing system.

V CONCLUSION

In this paper we have presented a new strong watermarking for 3D model. This new method is based

on Least significant bit algorithm, using double random phase modulation to make the embedding watermark

information more secure. The results will clearly show

that the presented watermarking procedure can tag objects with watermarks in a strong way. It is invisible

and the cropping attacks can be successfully handled.

Also the algorithm guarantees the good performance of the watermark robustness to attacks such as noise

addition; model simplification and affine attacks.The 3D images can be rotated and prevented from all the

attacks.

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