Use of the Optimization Steganography System - Essay Example

USING AN OPTIMIZATION STEGANOGRAPHY SYSTEM TO ARCHIVE DIGITAL MULTIMEDIA Insert After digital artifacts turned out to be an important facet of daily life, the need to preserve metadata in digital media has grown remarkably. The procedure of converting the metadata to digital archives, nevertheless, is limited by various challenges. The most significant challenge concerns the method sued to embed high payload capacity data into the digital multimedia while retaining the quality of the image. This paper will examine steganography as a remedy to the issue. Enabling the detection of genetic algorithm to boost the PSNR (peak signal–to-noise ratio) value by the high capacity information, will aid in solving the issue concerning digital multimedia archiving. Various data sizes are embedded onto the images and PSNR is also recorded for every image evaluated. Keywords—Steganography; LSB; Genetic Algorithm; Block mapping, message segmentation Introduction The current media and communication domain is subjugated by digital multimedia artifacts. In essence, digital content usage has turned out to be a reality to most individuals. Nevertheless, with the exception of its daily usage, digital media play a significant role in research and academics. Media facilitated the access to essential resources, training of essential skills and efficient time management. However, the information processing tasks which are performed by such media, still need to develop significantly. Through embedding; intended to connect two data types and improve the metadata capacity, steganography might come handy in resolving this issue. In the given research, the author seeks to undertake an analysis of digital multimedia and its effects on numerous aspects of human life, through the inclusion of spatial domain of steganography. As specified, the analysis will be undertaken through the least significant bit subscription scheme (LSB). The selection of research methodology was preset by the high levels of simplicity, remarkably huge hiding capacity, and the essence that it relies on such particulars of human eye as the insensitivity of low bit images. When stereo-image has not to be choked by the SDDS system, there is need to have messages embedded at a specific spot; thus offering maintenance to the statistical aspects on the cover page. Examining the spatial domain embedding technique, which limits the selection of a given position as a result of the message distribution regularity, it is clear that the process of embedding a message in a specific location would be faced by a number of challenges including: defining the changes in the spatial project. With the subject project, the mapping technique would be utilized so as to split the cover image under scrutiny into various major segments, and thus undertake an analysis, because this technique would assist in placing every single bit of the message in its place. This operation will underwrite to definition of the most rational frequency domain location thus driving the amount of static aspect interferences on the given spatial domain slot to minimum which may be realized by using the Genetic Algorithm (GA) (Dasguptaa, Mondalb & Duttac 2013). As a result, the objective of this paper would be researching the use of GA gauge in concealing metadata. This paper also reviews the various techniques of steganography. The third section of the paper illustrates how GA may be used for data hiding. The fourth section will examine the matric technique, while the fifth section will present an analysis of the offered algorithm. Lastly, the last section would present the result of the analysis and the recommendations. The paper is going to make use of Block substitution rather than the bit based substitution due to the substandard properties of the Bit based substitution. Review of steganography techniques Conventionally, steganography is defined as the ability of hiding explicit data in conventional messages in order to make it hard for user to understand the implications. Some of these techniques have been extensively used that they have been adopted widely, making them frequently used formats. Principally, steganography techniques are categorized into two major groups: the Spatial design approaches and Transform domain methods (Dasguptaa, Mondalb & Duttac 2013). The Transform approach supposes that images must be changed before the message is embedded into it. Working on this, approach, one should mention that it enables the hiding of the image’s message in the important segments of the image. This is the reason behind the need to differentiate between low, middle, and high frequency of the image when working with the transform approach. The essence that the rates of signal energy are specifically high in the lower frequencies elucidates on the reason why the image’s visibility is of high quality. Evaluating by these specifics, it is rational to presume that planting confidential or secret information in higher frequencies would serve to prevent the distortion of the image. Moreover, transformation domain approaches score highly as compared to other techniques since Transform Domain does not rely on the image’s format because the message is embedded into the image. This implies that Transform Domain approaches can withstand a lossless and a lossy compression (Dasguptaa, Mondalb & Duttac 2013). As opposed to the Transform Domain approaches, the Spatial Design approaches involve the embedding of essential message into pixels. It provides such techniques of embedding messages into the image in the form of LSB plane direct manipulation and cover image replacement with the bits to be hidden(Dasguptaa, Mondalb & Duttac 2013). However, the stated method is easy to identify (Chan & Chan, 2004). In its place, LSB matching centered on embedding the message into cover image has been proposed recently. As a result of the matching process which it involves, high rates of similarity rates and, thus, low detectability is implemented (Dasguptaa, Mondalb & Duttac 2013). This study is not the first occurrence of GA usage as a way of addressing the LSB matters. The GA has also been utilized in various studies so as to mask the data in the rightmost k LSBs of the host image (Dasguptaa, Mondalb & Duttac 2013), method proposed by Mielikainen being among the most current updates of the subject technique. GA Approach The genetic algorithm (GA) is a search and optimization approach founded on the doctrines of natural selection and genetics and. It enables population involving may people to evolve under itemized selection rubrics to a state which makes the most of the “fitness” while minimizing the cost function. The technique was advanced by John Holland in 1975. The algorithm begins with no knowledge if the right solution and relies completely on responses from the surrounding environment, evolution operators: crossover, reproduction and mutation, to attain the right solution. In beginning at various independent positions and searching in a parallel dimension, the algorithm circumvents local minima and congregates to sub optimal elucidations. This has facilitated the realization that GAs are capable of finding high performance expanses in complex domains without suffering the difficulties concomitant to high dimensionality; which , might occur with ascent decent methods or techniques that depend on derived information . References Dasguptaa, K., Mondalb, J. K. & Duttac, P. 2013, Optimized Video Steganography using Genetic Algorithm (GA), Procedia Technology, Vol.10, pp. 131–137 Read More