Practicing LSB Steganography in PCA Transform Field
Journal Title: International Journal of Innovative Research in Computer Science and Technology - Year 2017, Vol 5, Issue 3
Abstract
PCA conversion defines one main stream and two sub-streams for the color of homogenous areas. This subject has led to design some effective algorithms for the processing of colored images such as coloring, changing the color, compressing and Steganography. LSB is considered one of the oldest methods in secret information Steganography in images. There have been some various designs for this method. Using a compound method can lead to some new algorithms. This paper presents a new method for secret information Steganography.
Authors and Affiliations
Mahdi Koohi
Keywords
Related Articles
- EP ID EP748326
- DOI 10.21276/ijircst.2017.5.3.5
- Views 30
- Downloads 0
Measuring Maintainability of Object Oriented Design (MMOOD)
Measuring maintainability early in the software development life cycle mainly at design phase is a landmark of crucial significance to software designers, developers and quality controllers. Initial Measurement of object...
Modelling and Designing of NLP Interface to Database for Afaan Oromoo
Database management system has been widely used for storing and retrieving data. However, database is often hard to access the data since their interface is rigid in cooperating with user, due to that analysis of natural...
Concrete with Bottom Ash as a Replacement for Fine Aggregate Fails Mechanical Tests at Coal Washeries
Critical research is focused on the use of a variety of commercial waste products as alternatives to total, such as coal pyrophoric waste, heater slag, fibreglass waste products, elastic waste plastics, work slime pellet...
Performance Analysis of Optical Fiber Network from MAC Layer Perspective
Optical fiber provides infinite capacity to support the rapid traffic growth which is required to our information society. However, as demand is increased and technology has developed, we have realized that there is a fu...
A Primer on Generative Adversarial Networks
Generative Adversarial Networks (GANs) is a type of deep neural network architecture that utilizes unsupervised machine learning to generate data. They were presented in 2014, in a paper by Ian Goodfellow, Yoshua Bengio,...