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Study of Steganographic Techniques for Data Hiding

Authors: Pankaj M. Bhuyar; Dr. S. W. Mohod;

Study of Steganographic Techniques for Data Hiding

Abstract

Nowadays, the volume of data shared over the Internet is growing. As a result, data security is referred to as a major issue while processing data communications through the Internet. During communication procedures, everyone requires their data to remain secure. Steganography is the science and art of embedding audio, message, video, or image into another audio, image, video, or message to conceal it. It is used to secure confidential information from harmful attacks. This research offers a classification of digital steganography based on cover object categories, as well as a classification of steganalysis art. Image visual quality, structural similarity, mean square error, Image Fidelity, embedding capacity, and robustness are some of the important aspects of steganography. Researchers have made tremendous advances in the realm of digital steganography. Nonetheless, it is vital to emphasize the advantages and disadvantages of modern steganography techniques. This paper first presents a literature survey of information hiding, then classifies the proposed methods, and finally introduces a comparative study between the different methods.

Keywords

Steganography, Data Embedding, Steganalysis, Embedding Capacity

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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