steganography using genetic algorithm along with visual cryptography for wireless network application

Steganography using

Genetic Algorithm along with

Visual Cryptography

By, Aparna.N.K S4 MCA ROLL NO:1

Contents

Introduction Keywords Brief Overview Proposed System Step by Step Process Algorithm Description Functional Flow Diagram Performance Analysis Result

Introduction

The main aim of the project is to design a feasible RS resistance secure algorithm which combines the use of both steganography and cryptography with the goals of improving security, reliability, and efficiency for secret message. LSB steganography is combined with the Genetic Algorithm making it more secure from RS steganalysis.

Keywords:

Steganalysis: The detection of steganographically encoded

packages is called Steganalysis. To make steganalysis easier, several softwares are readily available on the internet as freeware or as a shareware. These softwares are capable of determining the irregularity of RGB patterns in the image, thus alerting the user that the particular image is a stego image.

Cryptography Cryptography is the practice and study of

techniques for secure communication in the presence of third parties (called adversaries).

Visual Cryptography Visual cryptography is a cryptographic technique which allows visual information to beencrypted in such a way that decryption becomes a mechanical operation that does not require a computer.

Genetic Algorithm In the computer science field of artificial

intelligence, a genetic algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems.

Brief Overview

Hiding information by embedding secret data into an innocuous medium is often referred to as steganography.

Steganography can be applied electronically by taking a message (a binary file) and some sort of cover (often a sound or image file) and combining both to obtain a “stego-object”.

The RS analysis is considered as one of the most famous steganalysis algorithm which has the potential to detect the hidden message by the statistic analysis of pixel values

Both the topic of steganography and visual cryptography has been considered as a distinct topic for image security.

The use of steganography in combination visual cryptography is a sturdy model and adds a lot of challenges to identifying such hidden and encrypted data

Proposed System

The proposed makes use of the LSB steganography combined with Genetic Algorithm. The Genetic algorithm is used to modify the pixel locations of the stego image thus enhancing the security level to new heights. The hidden data is now distributed all over the image rather than at the LSB of the stego image.

Step by Step process: An input image is accepted as cover image which is used

to hide the secret message.

An input image is accepted as cover image for the input message in plain text format.

After embedding the secret message in LSB (least significant bit) of the cover image, the pixel values of the steg-image are modified by the genetic algorithm to keep their statistic characters.

The user can select their targeted information in terms of plain text for embedding the secret message in LSB of the cover image.

The implications of the visual cryptography will enable the pixels value of the steg-image to keep their statistic character.

LSB steganography has low computation complexity and high embedding capacity, in which a secret binary sequence is used to replace the least significant bits of the host medium.

The algorithm in use is not secret but only the key used by the algorithm is kept as a secret between the two parties, this assumption is also known as Kerchoff’s principle in the field of cryptography.

Algorithm Description

The proposed project work consist of mainly two algorithms which are

(i) Steganography using Genetic Algorithm (ii) Visual Cryptography with Threshold. The application initiates with Steganography

module where the cover image will be encrypted to generate Stego image. The stegographic image generated in this module will act as an input for visual cryptographic module.

Steganography using Genetic Algorithm

Algorithm: SteganographyInput: Cover ImageOutput: Stego Image Step 1: Read the cover image. Step 2: Find out the pixel values of cover image. Step 3: Read the secret data character wise. Step 4: Convert each character into its equivalent ASCII

code. Step 5: ASCII code is converted into binary values. Step 6: Enter the secret key. Step 7: Secret data is converted into cipher data. Step 8: The stream of 8-bits (cipher data) are embedded into

LSB of each pixel of the cover image. Step 9: To apply Genetic Algorithm in the stego image the

pixel location should be modified.

Visual Cryptography with Threshold.

Algorithm: Visual CryptographyInput: Stego-ImageOutput: Encrypted Shares Step 1: Read Stego-Image generated. Step 2: The stego image is breaked into three layers

namely split-1, split-2, split-3 these three files are

containing the hidden data and to get the hidden data these three files have to be reconstructed perfectly. Step 3: The re-assembled picture and the extracted data will be gained again.

Functional Flow Diagram

The proposed system works in the following phases:SENDER SIDE:

Encryption Phase: The data to be encrypted is first read from the user through the keyboard using an appropriate GUI, designed in JAVA. The encryption algorithm used in the DES.

Encoding Phase: The encrypted image is then encoded into the

least significant bits of the image.

Pixel Modification Phase: Genetic algorithm is used to modify the pixel

locations for enhancing security and reliability.

RECEIVERS SIDE: Overlapping Phase: The two shares of the same image are needed to retrieve

the original information As the cipher data is distributed in both the images, it is impossible for anyone to get the data by obtaining just a single share of the image.

Decoding Phase :In the decoding phase, the cipher data is decoded from the stego image. The cipher data can be retrieved by the inverse process of encoding process that was employed at the sender side.

Decryption Phase :In the decryption phase, the cipher data is converted into the original data. The DES algorithm is used in the inverse manner using the same encryption key(secret key) as used during encryption of the original data.

Performance Analysis

The performance of the proposed system is experimented by performing stegnalysis and conducting benchmarking test for analysing parameters like Mean Squared Error (MSE) and Peak Signal to Noise Ratio.

Cover image : rice.png Size : 256*256

Mean Square Error (MSE) : 0.0678 Peak Signal-to-Noise Ratio (PSNR) : 59.8188db

After applying Genetic Algorithm the measured performance is shown in below

Mean Square Error (MSE) : 0.794 Peak Signal-to-Noise Ratio (PSNR) : 39.4011db After applying genetic algorithm all the pixel location are altered. Due to

the change the pixel location MSE and PSNR values are increased.

Result:

Encrypt Screen Message to be encrypted: “Sachin Ramesh Tendulkar- The LEGEND”

Encode ScreenAfter clicking on Encrypt Now at the bottom, the encrypted message is

shown. CipherText :

vbPG8nqjKQoW6N6ugkb4l+wbTz3c+EyRLPkW5nxVv1ZKFxHyPyTQoQ==

After clicking on Encode Now, a File Chooser is opened to choose the image file.

Conclusion

The proposed system has discussed implementation of securely using steganography using genetic algorithm along with visual cryptography. It can be concluded that when normal image security using steganographic and visual cryptographic technique is applied, it makes the task of the investigators unfeasible to decrypt the encoded secret message. The security features of the steganographic are highly optimized using genetic algorithm.

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