Designing an Efficient Image Encryption-Then-Compression System

via Prediction Error Clustering and Random Permutation

Under the guideMiss.Selvalakshmi Msc.,M.Phil.,

Submitted ByP.Iswarya Lakshmi

Regno:1163665

Abstract In many practical scenarios, image encryption has to be conducted prior to image compression. This has led to the problem

of how to design a pair of image encryption and compression algorithms such that compressing the encrypted images can

still be efficiently performed. In this process a highly efficient image encryption-then-compression (ETC) system.

The proposed image encryption scheme operated in the prediction error domain is shown to be able to provide a reasonably

high level of security and also demonstrate that an arithmetic coding-based approach can be exploited to efficiently

compress the encrypted images.

In this present the details of the three key components in our proposed ETC system, namely, image encryption conducted

by Alice, image compression conducted by Charlie, and the sequential decryption and decompression conducted by Bob.

From the perspective of the whole ETC system, the design of the encryption algorithm should simultaneously consider the

security and the ease of compressing the encrypted data.

Existing System Compression-then-Encryption (CTE) paradigm meets the requirements in many secure

transmission scenarios, the order of applying the compression and encryption needs to be

reversed in some other situations.

As the content owner, Sender is always interested in protecting the privacy of the image data

through encryption.

Sender has no incentive to compress her data, and hence, will not use her limited

computational resources to run a compression algorithm before encrypting the data.

This is especially true when Sender uses a resource-deprived mobile device. In contrast, the

channel provider Intermediate has an overriding interest in compressing all the network traffic

so as to maximize the network utilization.

Proposed System In this proposed system process with encryption then compression to share data.

In this proposed system to encrypted military information after images compress the

result.

A modified basis pursuit algorithm can then be applied to estimate the original

image from the compressed and encrypted data.

Encrypted file can be efficiently compressed by discarding the excessively rough

and fine information of coefficients in the transform domain.

EZW Algorithm The embedded zero tree wavelet algorithm (EZW) is a simple, yet remarkable effective,

image compression algorithm.

The bits in the bit stream are generated in order of importance, yielding a fully

embedded code.

The same image that would have been encoded at the bit rate corresponding to the

truncated stream.

The block information for every block to be encoded in EZW algorithm.

Architecture Diagram

Data Flow Diagram

ETC Context level Diagram

DFD level 0

Data Flow Diagram

DFD level 1

DFD level 2

Data Flow Diagram

DFD level 3

DFD level 4

Data Flow Diagram

DFD level 5

DFD level 6

Data Flow Diagram

Modules Preprocessing

Cluster division

Prediction Error and randomization

Image Encryption

Image encryption with Data

Image Compression

Result

Decompression

Decrypt Data

Cluster division

The pre processing image to be cluster into more than a few parts.  

It basically creates a matrix of images and draws on

each Bitmap adequate part of the large image.

The same concept could use for the picture boxes and put them in

the matrix. The picture box image split and places it.

Prediction Error and randomizationImage before encryption the prediction error will be test on the pretest

image to be able to provide a reasonably high level of security.

Split the image into rows then put those into a queue on the picture

boxes. Work on each row with individual picture boxes.

Add each image to a collection and persist the collection when all the

items are completed in the queue then randomization.

Image encryption with Data In the present data hiding at the sender side. These parts are constructed and implemented to satisfy

the algorithm must reduce the chances of statistical detection. 

The algorithm must provide robustness against a variety of image manipulation attacks. The stego-

image must not have any distortion artifacts. The algorithm must embedding capacity in order to

achieve the above requirements.

The images will be encrypt with data using the practice of hiding private or sensitive information

within something that appears to be nothing out to the usual way that are used to protect important

information.

Human senses are not trained to look for files that have information inside of them but visible only

image.

Image Compression Wavelet based on compression provides a very effective technique for military images. Military

image requires storage of large quantity of secret data. Due to the high bandwidth and capacity of

storage, military image must be compressed before transmission.

There are two categories for compression Lossy and Lossless method. Lossless compression

ensures complete data after reconstruction. In application of Lossy technique information is loss to

some degree. A popular method of image compression, namely, the embedded zero tree wavelet

(EZW).

The proposed image compression algorithm outperforms the EZW(embedded zero tree

wavelet) .The compression of an encrypted image with flexible compression ratio.

Result The receivers receive the encrypted image and the using decompresses the image after

to decrypt to get the original data.

Hardware Requirements

System : Dual core.

Hard Disk : 160 GB.

Monitor : 15 VGA Colour.

Mouse : Logitech.

Ram : 1 GB.

Software Requirements

Operating system : Windows XP/7.

Coding Language : C#.net

Tool : Visual Studio 2005

Database : SQL SERVER 2005

System Requirements