Improved Steganography Technique Research Articles

Improved Steganography Techniques for Different Types of Secret Data

Steganography is an interesting aspect of data hiding art where the secret data is embedded in a cover media without raising any suspicion about the existence of that data. Over the years, different steganography techniques for digital images have been presented to be applied in different applications such as security and privacy control. Some of these techniques are based on transforming the image from the spatial domain to the transform domain as a pre-processing step before embedding the secret data. This paper presents an improved steganography technique in the transform domain based on studying the effect of changing two major factors on the performance of steganography techniques. The paper illustrates the algorithms that have been implemented for the study purposes. The results of this study proved that the two major factors that can affect the performance of the steganography technique are the transform type and the data embedding method. According to this study an improved steganography technique is presented based on Slantlet transform (SLT) and a new data embedding method. The experimental results proved the efficiency of the proposed technique in comparison with a previous SLT-based steganography technique.

A New Approach for Information Security using an Improved Steganography Technique

This research paper proposes a secured, robust approach of information security using steganography. It presents two component based LSB (Least Significant Bit) steganography methods for embedding secret data in the least significant bits of blue components and partial green components of random pixel locations in the edges of images. An adaptive LSB based steganography is proposed for embedding data based on the data available in MSB's (Most Significant Bits) of red, green, and blue components of randomly selected pixels across smooth areas. A hybrid feature detection filter is also proposed that performs better to predict edge areas even in noisy conditions. AES (Advanced Encryption Standard) and random pixel embedding is incorporated to provide two-tier security. The experimental results of the proposed approach are better in terms of PSNR and capacity. The comparison analysis of output results with other existing techniques is giving the proposed approach an edge over others. It has been thoroughly tested for various steganalysis attacks like visual analysis, histogram analysis, chi-square, and RS analysis and could sustain all these attacks very well.