20-11-2012, 04:11 PM
Reduce Distortion in Stenography using Syndrome Trellis Codes
1Reduce Distortion.doc (Size: 1.61 MB / Downloads: 542)
INTRODUCTION
In steganography there exist two mainstream approaches to empirical covers, such as digital media objects: steganography designed to preserve a chosen cover model and steganography minimizing a heuristically-defined embedding distortion. The strong argument for the former strategy is that provable undetectability can be achieved w.r.t. a specific model. The disadvantage is that an adversary can usually rather easily identify statistical quantities that go beyond the chosen model that allow reliable detection of embedding changes. The latter strategy is more pragmatic—it abandons modeling the cover source and instead tells the steganographer to embed payload while minimizing a distortion function. In doing so, it gives up any ambitions for perfect security.
Although this may seem as a costly sacrifice, it is not, as empirical covers have been argued to be in cognizable , which prevents model-preserving approaches from being perfectly secure as well. While we admit that the relationship between distortion and steganographic security is far from clear, embedding while minimizing a distortion function is an easier problem than embedding with a steganographic constraint (preserving the distribution of covers). It is also more flexible, allowing the results obtained from experiments with blind steganalyzers to drive the design of the distortion function.
PROBLEM STATEMENT
In steganography the problem of embedding while minimizing a distortion function is occured. We state the performance bounds and define some numerical quantities that will be used to compare coding methods w.r.t. each other and to the bounds. We assume the sender obtains her payload in the form of a pseudo-random bit stream, such as by compressing or encrypting the original message. The total distortion is assumed to be a sum of per-element distortions. Both the payload-limited sender (minimizing the total distortion while embedding a fixed payload) and the distortion-limited sender (maximizing the payload while introducing a fixed total distortion) are considered. Without any loss of performance, the non binary case is decomposed into several binary cases by replacing individual bits in cover elements.
Efficient steganography based on a data decomposition mechanism
A novel steganographic scheme based on a mechanism of data decomposition is proposed. In this scheme, a secret message is represented as a sequence of digits in a notational system with a prime base. Each digit block is decomposed into a number of shares. These shares are then embedded in different cover images respectively. In each cover, a share is carried by a group of cover pixels and, at most, only one pixel in the group is increased or decreased by 1. That implies a high embedding efficiency, and therefore distortion introduced to the covers is low, leading to enhanced imperceptibility of the secret message. A further advantage of the scheme is that, even a part of stego-images are lost during transmission, the receiver can still extract embedded messages from the surviving covers.
Modified matrix encoding technique for minimal distortion steganography
It is well known that all information hiding methods that modify the least significant bits introduce distortions into the cover objects. Those distortions have been utilized by steganalysis algorithms to detect that the objects had been modified. It has been proposed that only coefficients whose modification does not introduce large distortions should be used for embedding. In this paper we propose an efficient algorithm for information hiding in the LSBs of JPEG coefficients. Our algorithm uses modified matrix encoding to choose the coefficients whose modifications introduce minimal embedding distortion. We derive the expected value of the embedding distortion as a function of the message length and the probability distribution of the JPEG quantization errors of cover images. Our experiments show close agreement between the theoretical prediction and the actual embedding distortion. Our algorithm can be used for both steganography and fragile watermarking as well as in other applications in which it is necessary to keep the distortion as low as possible.
Stego image and Stego-Analysis System
There are several approaches in classifying Steganographic systems. One could categorize them according to the type of covers used for secret communication or according to the cover modifications applied in the embedding process. The second approach will be followed in this section, and the Steganographic methods are grouped in six categories, although in some cases an exact classification is not possible.
Still imagery steganography
The most widely used technique today is hiding of secret messages into a digital image. This steganography technique exploits the weakness of the human visual system (HVS). HVS cannot detect the variation in luminance of color vectors at higher frequency side of the visual spectrum. A picture can be represented by a collection of color pixels. The individual pixels can be represented by their optical characteristics like 'brightness', 'chroma' etc. Each of these characteristics can be digitally expressed in terms of 1s and 0s.
For example: a 24-bit bitmap will have 8 bits, representing each of the threecolor values (red, green, and blue) at each pixel. If we consider just the blue there will be 28 different values of blue. The difference between 11111111 and 11111110 in the value for blue intensity is likely to be undetectable by the human eye. Hence, if the terminal recipient of the data is nothing but human visual system (HVS) then the Least Significant Bit (LSB) can be used for something else other than color information. This technique can be directly applied on digital image in bitmap format as well as for the compressed image format like JPEG. In JPEG format, each pixel of the
image is digitally coded using discrete cosine transformation (DCT). The LSB of encoded DCT components can be used as the carriers of the hidden message.
SYSTEM ANALYSIS
The Systems Development Life Cycle (SDLC), or Software Development Life Cycle in systems engineering, information systems and software engineering, is the process of creating or altering systems, and the models and methodologies that people use to develop these systems.
In software engineering the SDLC concept underpins many kinds of software development methodologies. These methodologies form the framework for planning and controlling the creation of an information system the software development process.
SDLC Methodology:
This document play a vital role in the development of life cycle (SDLC) as it describes the complete requirement of the system. It means for use by developers and will be the basic during testing phase. Any changes made to the requirements in the future will have to go through formal change approval process.
SPIRAL MODEL was defined by Barry Boehm in his 1988 article, “A spiral Model of Software Development and Enhancement. This model was not the first model to discuss iterative development, but it was the first model to explain why the iteration models.
As originally envisioned, the iterations were typically 6 months to 2 years long. Each phase starts with a design goal and ends with a client reviewing the progress thus far. Analysis and engineering efforts are applied at each phase of the project, with an eye toward the end goal of the project.
EXISTING SYSTEM
• In special domain, the hiding process such as least significant bit(LSB) replacement, is done in special domain, while transform domain methods; hide data in another domain such as wavelet domain.
• Least significant bit (LSB) is the simplest form of Steganography. LSB is based on inserting data in the least significant bit of pixels, which lead to a slight change on the cover image that is not noticeable to human eye. Since this method can be easily cracked, it is more vulnerable to attacks.
• LSB method has intense affects on the statistical information of image like histogram. Attackers could be aware of a hidden communication by just checking the Histogram of an image. A good solution to eliminate this defect was LSB matching. LSB-Matching was a great step forward in Steganography methods and many others get ideas from it.
PROPOSED SYSTEM
In the proposed system it is planned to introduce a method that embed 2 bits information in a pixel and alter one bit from one bit plane but the message does not necessarily place in the least significant bit of pixel and second less significant bit plane and fourth less significant bit plane can also host the massage.
Since in our method for embedding two bits message we alter just one bit plane, fewer pixels would be manipulated during embedding message in an image and it is expected for the steganalysis algorithm to have more difficulty detecting the covert communication. It is clear that in return complexity of the system would increase. In our method there are only three ways that a pixel is allowed to be changed:Its least significant Bit would alter (So the gray level of the pixel would increased or decreased by one level).The second less significant bit plane would alter (So the gray level of the pixel would increase or decrease by two levels).The fourth less significant bit plane would alter (So the gray level of the pixel would increase or decrease by eight levels).