4,075 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
Image steganography based on DNA sequence translation properties
Digital communication has become a vital part of daily life nowadays, many applications are using internet-based communication and here the importance of security rose to have a secure communication between two parties to prevent authorized access to sensitive data. These requirements led to a number of research in information security that has been done in the past two decades. Cryptography and steganography are the two main methods that are being used for information security. Cryptography refers to techniques that encrypt a message to be sent to a destination using different methods to be done. On the other hand, steganography is the science of hiding information from others using another cover message or media such as image, audio, video, and DNA sequence. This paper proposed a new method to hide information in an image using the least significant bit (LSB) based on Deoxyribonucleic Acid (DNA) sequence. To accomplish this, the proposed scheme used properties of DNA sequence when codons that consist of three nucleotides are translated to proteins. The LSB of two pixels from the image are taken to represent a codon and then translate them to protein. The secret message bits are injected into codons before the translation process which slightly distorts the image and makes the image less suspicious and hard to detect the hidden message. The experimental results indicate the effeteness of the proposed method
Data hiding using integer lifting wavelet transform and DNA computing
DNA computing widely used in encryption or hiding the data. Many researchers have proposed many developments of encryption and hiding algorithms based on DNA sequence to provide new algorithms. In this paper data hiding using integer lifting wavelet transform based on DNA computing is presented. The transform is applied on blue channel of the cover image. The DNA encoding used to encode the two most significant bits of LL sub-band. The produced DNA sequence used for two purpose, firstly, it use to construct the key for encryption the secret data and secondly to select the pixels in HL, LH, HH sub-bands for hiding in them. Many measurement parameters used to evaluate the performance of the proposed method such PSNR, MSE, and SSIM. The experimental results show high performance with respect to different embedding rate
A perceptual hash function to store and retrieve large scale DNA sequences
This paper proposes a novel approach for storing and retrieving massive DNA
sequences.. The method is based on a perceptual hash function, commonly used to
determine the similarity between digital images, that we adapted for DNA
sequences. Perceptual hash function presented here is based on a Discrete
Cosine Transform Sign Only (DCT-SO). Each nucleotide is encoded as a fixed gray
level intensity pixel and the hash is calculated from its significant frequency
characteristics. This results to a drastic data reduction between the sequence
and the perceptual hash. Unlike cryptographic hash functions, perceptual hashes
are not affected by "avalanche effect" and thus can be compared. The similarity
distance between two hashes is estimated with the Hamming Distance, which is
used to retrieve DNA sequences. Experiments that we conducted show that our
approach is relevant for storing massive DNA sequences, and retrieving them
JPEG steganography with particle swarm optimization accelerated by AVX
Digital steganography aims at hiding secret messages in digital data transmitted over insecure channels. The JPEG format is prevalent in digital communication, and images are often used as cover objects in digital steganography. Optimization methods can improve the properties of images with embedded secret but introduce additional computational complexity to their processing. AVX instructions available in modern CPUs are, in this work, used to accelerate data parallel operations that are part of image steganography with advanced optimizations.Web of Science328art. no. e544
A Novel Latin Square Image Cipher
In this paper, we introduce a symmetric-key Latin square image cipher (LSIC)
for grayscale and color images. Our contributions to the image encryption
community include 1) we develop new Latin square image encryption primitives
including Latin Square Whitening, Latin Square S-box and Latin Square P-box ;
2) we provide a new way of integrating probabilistic encryption in image
encryption by embedding random noise in the least significant image bit-plane;
and 3) we construct LSIC with these Latin square image encryption primitives
all on one keyed Latin square in a new loom-like substitution-permutation
network. Consequently, the proposed LSIC achieve many desired properties of a
secure cipher including a large key space, high key sensitivities, uniformly
distributed ciphertext, excellent confusion and diffusion properties,
semantically secure, and robustness against channel noise. Theoretical analysis
show that the LSIC has good resistance to many attack models including
brute-force attacks, ciphertext-only attacks, known-plaintext attacks and
chosen-plaintext attacks. Experimental analysis under extensive simulation
results using the complete USC-SIPI Miscellaneous image dataset demonstrate
that LSIC outperforms or reach state of the art suggested by many peer
algorithms. All these analysis and results demonstrate that the LSIC is very
suitable for digital image encryption. Finally, we open source the LSIC MATLAB
code under webpage https://sites.google.com/site/tuftsyuewu/source-code.Comment: 26 pages, 17 figures, and 7 table
Review on DNA Cryptography
Cryptography is the science that secures data and communication over the
network by applying mathematics and logic to design strong encryption methods.
In the modern era of e-business and e-commerce the protection of
confidentiality, integrity and availability (CIA triad) of stored information
as well as of transmitted data is very crucial. DNA molecules, having the
capacity to store, process and transmit information, inspires the idea of DNA
cryptography. This combination of the chemical characteristics of biological
DNA sequences and classical cryptography ensures the non-vulnerable
transmission of data. In this paper we have reviewed the present state of art
of DNA cryptography.Comment: 31 pages, 12 figures, 6 table
A forensics software toolkit for DNA steganalysis.
Recent advances in genetic engineering have allowed the insertion of artificial DNA strands into the living cells of organisms. Several methods have been developed to insert information into a DNA sequence for the purpose of data storage, watermarking, or communication of secret messages. The ability to detect, extract, and decode messages from DNA is important for forensic data collection and for data security. We have developed a software toolkit that is able to detect the presence of a hidden message within a DNA sequence, extract that message, and then decode it. The toolkit is able to detect, extract, and decode messages that have been encoded with a variety of different coding schemes. The goal of this project is to enable our software toolkit to determine with which coding scheme a message has been encoded in DNA and then to decode it. The software package is able to decode messages that have been encoded with every variation of most of the coding schemes described in this document. The software toolkit has two different options for decoding that can be selected by the user. The first is a frequency analysis approach that is very commonly used in cryptanalysis. This approach is very fast, but is unable to decode messages shorter than 200 words accurately. The second option is using a Genetic Algorithm (GA) in combination with a Wisdom of Artificial Crowds (WoAC) technique. This approach is very time consuming, but can decode shorter messages with much higher accuracy
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