1,499 research outputs found
DNA Steganalysis Using Deep Recurrent Neural Networks
Recent advances in next-generation sequencing technologies have facilitated
the use of deoxyribonucleic acid (DNA) as a novel covert channels in
steganography. There are various methods that exist in other domains to detect
hidden messages in conventional covert channels. However, they have not been
applied to DNA steganography. The current most common detection approaches,
namely frequency analysis-based methods, often overlook important signals when
directly applied to DNA steganography because those methods depend on the
distribution of the number of sequence characters. To address this limitation,
we propose a general sequence learning-based DNA steganalysis framework. The
proposed approach learns the intrinsic distribution of coding and non-coding
sequences and detects hidden messages by exploiting distribution variations
after hiding these messages. Using deep recurrent neural networks (RNNs), our
framework identifies the distribution variations by using the classification
score to predict whether a sequence is to be a coding or non-coding sequence.
We compare our proposed method to various existing methods and biological
sequence analysis methods implemented on top of our framework. According to our
experimental results, our approach delivers a robust detection performance
compared to other tools
A Pseudo DNA Cryptography Method
The DNA cryptography is a new and very promising direction in cryptography
research. DNA can be used in cryptography for storing and transmitting the
information, as well as for computation. Although in its primitive stage, DNA
cryptography is shown to be very effective. Currently, several DNA computing
algorithms are proposed for quite some cryptography, cryptanalysis and
steganography problems, and they are very powerful in these areas. However, the
use of the DNA as a means of cryptography has high tech lab requirements and
computational limitations, as well as the labor intensive extrapolation means
so far. These make the efficient use of DNA cryptography difficult in the
security world now. Therefore, more theoretical analysis should be performed
before its real applications.
In this project, We do not intended to utilize real DNA to perform the
cryptography process; rather, We will introduce a new cryptography method based
on central dogma of molecular biology. Since this method simulates some
critical processes in central dogma, it is a pseudo DNA cryptography method.
The theoretical analysis and experiments show this method to be efficient in
computation, storage and transmission; and it is very powerful against certain
attacks. Thus, this method can be of many uses in cryptography, such as an
enhancement insecurity and speed to the other cryptography methods. There are
also extensions and variations to this method, which have enhanced security,
effectiveness and applicability.Comment: A small work that quite some people asked abou
Combining and Steganography of 3D Face Textures
One of the serious issues in communication between people is hiding
information from others, and the best way for this, is deceiving them. Since
nowadays face images are mostly used in three dimensional format, in this paper
we are going to steganography 3D face images, detecting which by curious people
will be impossible. As in detecting face only its texture is important, we
separate texture from shape matrices, for eliminating half of the extra
information, steganography is done only for face texture, and for
reconstructing 3D face, we can use any other shape. Moreover, we will indicate
that, by using two textures, how two 3D faces can be combined. For a complete
description of the process, first, 2D faces are used as an input for building
3D faces, and then 3D textures are hidden within other images.Comment: 6 pages, 10 figures, 16 equations, 5 section
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