Printer Friendly

A novel randomised cryptographic technique.


Information Security generally falls under three categories namely Confidentiality, Integrity and Availability.


Data confidentiality: Assures that private or confidential information is not made available or disclosed to unauthorized individuals.

Privacy: Assures that individuals control or influence what information related to them may be collected and stored and by whom and to whom that information may be disclosed.


Data integrity: Assures that information and programs are changed only in a specified and authorized manner.

System integrity: Assures that a system performs its intended function in an unimpaired manner, free from deliberate or inadvertent unauthorized manipulation of the system.


Assures that systems work promptly and service is not denied to authorized users.

These three concepts form what is often referred to as the CIA triad (Figure 1.1). The three concepts embody the fundamental security objectives for both data and for information and computing services.


literature survey:

In this paper [2], Most state-of-the-art binary image steganographic techniques only consider the flipping distortion according to the human visual system (HVS), which will be not secure when they are attacked by steganalyzers. In this paper, a binary image steganographic scheme that aims to minimize the embedding distortion on the texture is presented. We extract the complement, rotation, and mirroring-invariant local texture patterns (crmiLTPs) from the binary image first. The weighted sum of crmiLTP changes when flipping one pixel is then employed to measure the flipping distortion corresponding to that pixel. By testing on both simple binary images and the constructed image dataset, we show that the proposed measurement can well describe the distortions on both visual quality and statistics. Based on the proposed measurement, a practical steganographic scheme is developed. The steganographic scheme generates the cover vector by dividing the scrambled image into superpixels. Thereafter, the syndrome-trellis code (STC) is employed to minimize the designed embedding distortion. Experimental results have demonstrated that the proposed steganographic scheme can achieve statistical security without degrading the image quality or the embedding capacity.

In this paper [5], The accuracy of steganalysis in digital images primarily depends on the statistical properties of neighboring pixels, which are strongly affected by the image acquisition pipeline as well as any processing applied to the image. In this paper, we study how the detectability of embedding changes is affected when the cover image is downsampled prior to embedding. This topic is important for practitioners because the vast majority of images posted on websites, image sharing portals, or attached to e-mails are downsampled. It is also relevant to researchers as the security of steganographic algorithms is commonly evaluated on databases of downsampled images. In the first part of this paper, we investigate empirically how the steganalysis results depend on the parameters of the resizing algorithm-the choice of the interpolation kernel, the scaling factor (resize ratio), antialiasing, and the downsampled pixel grid alignment. We report on several novel phenomena that appear valid universally across the tested cover sources, steganographic methods, and steganalysis features. This paper continues with a theoretical analysis of the simplest interpolation kernel--the box kernel. By fitting a Markov chain model to pixel rows, we analytically compute the Fisher information rate for any mutually independent embedding operation and derive the proper scaling of the secure payload with resizing. For least significant bit (LSB) matching and a limited range of downscaling, the theory fits experiments rather well, which indicates the existence of a new scaling law expressing the length of the secure payload when the cover size is modified by subsampling.

In this paper [8], Steganography is the problem of hiding secret messages in "innocent-looking" public communication so that the presence of the secret messages cannot be detected. This paper introduces a cryptographic formalization of steganographic security in terms of computational indistinguishability from a channel, an indexed family of probability distributions on cover messages. We use cryptographic and complexity-theoretic proof techniques to show that the existence of one-way functions and the ability to sample from the channel are necessary conditions for secure steganography. We then construct a steganographic protocol, based on rejection sampling from the channel, that is provably secure and has nearly optimal bandwidth under these conditions. This is the first known example of a general provably secure steganographic protocol. We also give the first formalization of "robust" steganography, where an adversary attempts to remove any hidden messages without unduly disrupting the cover channel. We give a necessary condition on the amount of disruption the adversary is allowed in terms of a worst case measure of mutual information. We give a construction that is provably secure and computationally efficient and has nearly optimal bandwidth, assuming repeatable access to the channel distribution.

In this paper [10], Steganography is the art of hiding data into a media in such a way that the presence of data can't be detected when the communication is taking place. This paper provides a review of recent achievements of LSB based spatial domain steganography that have an improved steganography's ultimate objectives, which are undetectable, robustness and capacity of hidden data. These techniques can help researchers in understanding about image steganography and various techniques of hiding data in an image. Along with this, two new methods are proposed one for hiding secret message into cover image and the second is hiding a grey scale secret image into another grey scale image. These methods uses 4-states #table that generates pseudo random numbers which are used for hiding secret information. These methods provide higher security because secret information is hidden at different position of LSB of image depending on pseudo numbers generated by the #table.

In this paper [15], In order to improve the security of steganography, this paper studied image steganography combined with pre-processing of DES encryption. When transmitting the secret information, firstly, encrypt the information intended to hide by DES encryption is encrypted, and then is written in the image through the LSB steganography. Encryption algorithm improves the lowest matching performance between the image and the secret information by changing the statistical characteristics of the secret information to enhance the anti-detection of the image steganography. The experimental results showed that the antidetection robustness of image steganography combined with pre-processing of DES encryption is found much better than the way using LSB steganography algorithms directly.

Proposed Solution:

Here we are proposing a methodology which increases the security of the system to a very high extent. In our method, we have proposed a novel method which increases the information security. For implementation purpose, we have taken an audio data embedded inside a video carrier. The idea is to encrypt the information first using some recent advanced encryption techniques. The algorithm used is changed every time in order to achieve a better security and then XORing the resulting data with a Pseudo-Random sequence and then finally performing the encryption. As you can see, three level of security is added to the data. First the encryption, second the XOR operation and finally the Steganography. Thus this method provides a high security while sending the information. And the hackers will not be able to perform Cryptanalysis as the cipher text is sent after the XOR operation and without the same Pseudo-Random Sequence, none of the data can be retrieved.

Now let us look into the individual steps in detail. The first step is to perform the encryption using some modern encryption techniques. The encryption algorithm would be chosen in an arbitrary manner so as to make cryptanalysis much more complicated. Since in this method the encryption method itself is unknown, the hackers would find it hard to perform the cryptanalysis. The main problem here is to establish the synchronization between the sender and the receiver such that so that the transmitter and the receiver knows which algorithm to use for a particular message. One simple way of solving this problem is to have a encryption type data embedded inside the output. The receiver can use this variable to find the type of encryption based on a look up table that is known only to the sender and the receiver.

Secondly many advancements has come in the cryptanalysis techniques in the recent days and using which the data can be retrieved using simple brute-force attack. Hence in this proposed solution, we have added a second level of encryption wherein the resulting cipher text is encrypted again using a simple XOR operation. The encryption key used in the XOR operation is chosen using a pseudo random sequence. The biggest strength of the XOR operation is that the original data cannot be retrieved without XORing again with the same encryption key.

In order to increase the security to a greater extent, we are not using the same key in the XOR operation. Each byte of the cipher text is XORed with a different encryption key. The encryption key is formed using a pseudorandom sequence. Both the sender and the receiver will be having the seed value for the pseudorandom sequence. With that seed value, we will be able to exactly produce the pseudorandom sequence at both the sides. This seed value can also be changed for every encryption so as to increase the complexity. And with the resulting output, we will perform the steganography. This data is embedded inside the LSB bits of the carrier video data.

In order to improve the signal to noise ratio, we will embed these encrypted data inside the alternative frames of the video sequence. Since any video is comprised of a sequence of video frames, we can simply ignore the alternate frames and embed the information in the remaining frames. In this case the actual video is the signal and the embedded information is the noise. Thus with this method the noise is inserted uniformly among the video signals rather than just making it concentrated at the start of the video signals. And because of the noise(data) being distributed randomly throughout the video sequence, it would be very difficult for the hacker to find out the location of the embedded information.

Also since we are using the adjacent frames, it would also be difficult for the hacker to perform any pattern analysis without having prior knowledge on where the data is embedded in the video.

Also since in these days, we seldom use the uncompressed data format. Many video compression techniques has come which will compress the data to a very great extent. For instance in case of an MPEG image, the video data is comprised of i-frame, p-frame and b-frame. The i-frame is the initial frame and will compose of the compressed image of that particular frame. The p-frame and the b-frame will compose of the differences from the original i-frame. These differences will be compressed and stored in an mpeg video signal. In such cases, directly replacing the LSB of all bytes will decrease the SNR value and hence we should only select the co-efficient with has less significance and should embed data in the LSB of those data and thereby increasing the SNR value.

A similar approach can be followed for any other compressed video technique. The first point is to identify the bytes that are of very less significance and then embed the information inside it. However there are certain cases where a high level compression is performed and will not have any data which has least significance. In such cases there is no other way to improve the SNR. However we can randomize the way the data is embedded inside the carrier.

Flowchart--TX Side:


Flowchart--RX Side:



Conventional encryption methods will have one common algorithm and a key used for encryption and decryption. When the hacker finds out about the key and algorithm, the successive messages can be easily decrypted. However in our case the encryption algorithm is also changed on a rotation basis thereby increasing the strength of the algorithm. The PN Sequence used can also be changed every time so for a hacker, all the parameters has to be newly found every time thereby increasing the complexity algorithm.
S.No    Parameter               Traditional    Proposed

1       Number of unknowns      1              4
2       Encryption Algorithm    Constant       Changed everytime using
                                                 a rotor machine
3       Secret Key              Constant       Changed everytime
4       Algorithm Complexity    Medium         Very high


Now let us analyze the SNR value of the transmitted bit stream. In our case, we have video as the input and we are embedding the secret information inside the video signals. Although the data being embedded is required, we still consider it as noise as it will degrade the clarity of the signal. By considering that in mind, we will calculate the SNR of a video who is of resolution 480x360. Given that the resolution the total number of bits is 480x360x24(24 for 3 colors with 8-bit resolution) and we embed secret data of 1 Byte in every 6 Bytes in the first frame and no bits in the next frame. Which brings down the noise level by half meaning that we embed 1 Byte of noise(secret data) in every 12 Bytes of data. And also the affected bits are chosen to be in the least significant positions and thereby decreasing the noise impact. Thus the proposed system also has a better signal to noise level compared to the rest of the methodologies. The traditional way of calculating the SNR cannot be applied here as we are actually comparing two frames.


I would like to thank the almighty god for all the blessings he bestowed on me, which drove me to the successful completion of this project. I would like to extent my heartfelt gratitude to our respected Dean of Anna University, Regional Office, Madurai Dr. K. SIVAKUMAR Ph.D., who is the guiding light for all the activities in our university.

I express my sincere thanks to our Head of the Department Dr. V. MALATHI Ph.D., for her co-operation, guidance and suggestions at every stage of my project. I would like to extend my sincere thanks to my Project Co-ordinator Mr. S. VELUCHAMY M.E., Faculty of ECE, for his encouragement and constant support throughout my course.

My sincere and special thanks to my internal guide Mr .S. VELUCHAMY M.E., Faculty of ECE, for guiding me in each and every aspect of this project. I also thank all the teaching staff and non-teaching staff of the Department of ECE, my parents, and all my friends for their help and support to complete this phase of the project successfully.


As we can see from the above paper and the comparison results, it is clear that the proposed solution is far better than any conventional methods. This proposed method has way too many unknowns for the hacker to decrypt the message thus providing the best information security possible.


[1.] Andrew, D. Ker and Tomas Pevny, 2014. 'The Steganographer is the Outlier : Realistic Large-Scale Steganalysis', IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 9(9).

[2.] Bingwen Feng, Wei Lu, Wei Sun, 2015. 'Secure Binary Image Steganography Based on Minimizing The Distortion on The Texture', IEEE TRANSATIONS ON INFORMATION FORENSICS AND SECURITY, 10: 2.

[3.] Charlie Kaufman, 2004. 'Network Security Private Communication in Public World', 2nd edition, Prentice Hall of India New Delhi.

[4.] Graeme Bell and Yeuan-Kuen Lee, 2010. 'A Method for Automatic Identification of Signatures of Steganography Software', IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 5: 2.

[5.] Jan Kodovsky and Jessica Fridrich, 2014. 'Effect of Image Downsampling on Steganographic Security', IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 9: 5.

[6.] Kuo-Chen Wu and Chung-Ming Wang, 2015. 'Steganography Using Reversible Texture Synthesis', IEEE TRANSACTIONS ON IMAGE PROCESSING, 24: 1.

[7.] Linjie Guo, Jiangqun Ni and Yun Qing Shi, 2014. 'Uniform Embedding for Efficient JPEG Steganography', IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 9: 5.

[8.] Nicholas Hopper, Luis von Ahn and John Langford, 2009. 'Provably Secure Steganography', IEEE TRANSACTIONS ON COMPUTERS, 58: 5.

[9.] Nichols, K and P.C. Lekkas, 2002. 'Wireless Security' Mc Graw Hill.

[10.] Prashanti, G and K. Sandhyarani, 2015. 'A New Approach for Data Hiding with LSB Steganography', Springer International Publishing, Switzerland.

[11.] Vojfech Holub and Jessica Fridrich, 2015. 'Low-Complexity Features for JPEG Steganalysis Using Undecimated DCT', IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 10: 2.

[12.] William Stallings, 2004. 'Network Security Essentials', 2nd edition, Prentice Hall of India New Delhi.

[13.] William Stallings, 2004. 'Cryptography and Network Security', 3rd edition, Prentice Hall of India, New Delhi.

[14.] Wojciech Mazurczyk and Luca Caviglione, 2015. 'Steganography in Modem Smartphones and Mitigation Techniques', IEEE COMMUNICATION SURVEYS & TUTORIALS, 17: 1.

[15.] Yang Ren-er, Zheng Zhiwei, Tao Shun, Ding Shileic, 2014. 'Image Steganography Combined with DES Encryption Pre-processing', Sixth International Conference on Measuring Technology and Mechatronics Automation

(1) S. Eben Gnana Pradeep, (2) S. Velusamy, (3) R. Anandha Murugan

(1) Department of Electronics and Communication Engineering, Anna University Regional Office, Madurai 625 007 INDIA

(2) Faculty, Department of Electronics and Communication Engineering, Anna University Regional Office, Madurai 625 007 INDIA

(3) Department of Computer Science Engineering, K.L.N. College of Engineering, Pottapalayam, Sivagangai 630 612 INDIA

Received 27 May 2016; Accepted 28 June 2016; Available 12 July 2016

Address For Correspondence:

S. Eben Gnana Pradeep, Department of Electronics and Communication Engineering, Anna University Regional Office, Madurai 625 007 INDIA.

Tel: 91-94860 76650; E-mail:
COPYRIGHT 2016 American-Eurasian Network for Scientific Information
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2016 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Pradeep, S. Eben Gnana; Velusamy, S.; Murugan, R. Anandha
Publication:Advances in Natural and Applied Sciences
Date:Jun 30, 2016
Previous Article:Performance improvement of by-passing in fuzzy dataclustering in wireless sensor networks.
Next Article:Prediction of periodontitis in patients using big data analytics.

Terms of use | Privacy policy | Copyright © 2019 Farlex, Inc. | Feedback | For webmasters