Deep Learning Applied to Steganalysis of Digital Images: A Systematic Review

Tabares-Soto, Reinel; Raul, Ramos-Pollan; Isaza, Gustavo

doi:10.1109/access.2019.2918086

Cited by 60 publications

(29 citation statements)

References 54 publications

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“…Generally, steganalysis is classified into two phases-the first phase is separating and analyzing the distinctive features and properties of images. The second is the classification phase that uses classifiers such as SVM (Support Vector Machines) and Ensemblers [9]. In recent times, various deep learning techniques have made it possible to merge these two phases and carry them out parallelly to obtain positive results.…”

Section: Related Workmentioning

confidence: 99%

Steganography Techniques Using Convolutional Neural Networks

Kumar¹,

Laddha²,

Aniket³

et al. 2020

RCES

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Steganography has been used since centuries for concealment of messages in a cover media where messages were physically hidden. The goal in our paper is to hide digital messages using modern steganography techniques. An N * N RGB pixel secret message (either text or image) is to be transmitted in another N * N RGB container image with minimum changes in its contents. The cover image also called the carrier can be publicly visible. In this paper, along with LSB encoding, deep learning modules using the Adam algorithm are used to train the model that comprises a hiding network and a reveal network. The encoder neural network determines where and how to place the message, dispersing it throughout the bits of cover image. The decoder network on the receiving side, which is simultaneously trained with the encoder, reveals the secret image. The main aspect of this work is it produces minimal distortion to the secret message. Thus, preserving its integrity. Also, other steganography softwares cannot be used to reveal the message since the model is trained using a deep learning algorithm which complicates its steganalysis. The network is only trained once, irrespective of the different container images and secret messages given as inputs. Thus, this work has wide and secure applications in many fields.

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Section: Related Workmentioning

confidence: 99%

Steganography Techniques Using Convolutional Neural Networks

Kumar¹,

Laddha²,

Aniket³

et al. 2020

RCES

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“…The strategy used was based on the guidelines proposed by Wen et al (2012) and used in Franco-Bedoya et al (2017) and Reinel, Raul & Gustavo (2019). To define the need for a systematic review, we searched through published and available reviews (secondary studies) on the topic of interest.…”

Section: Identification Of the Need For A Reviewmentioning

confidence: 99%

A systematic review of the application of machine learning in the detection and classification of transposable elements

Orozco-Arias

Isaza

Guyot

et al. 2019

PeerJ

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Background Transposable elements (TEs) constitute the most common repeated sequences in eukaryotic genomes. Recent studies demonstrated their deep impact on species diversity, adaptation to the environment and diseases. Although there are many conventional bioinformatics algorithms for detecting and classifying TEs, none have achieved reliable results on different types of TEs. Machine learning (ML) techniques can automatically extract hidden patterns and novel information from labeled or non-labeled data and have been applied to solving several scientific problems. Methodology We followed the Systematic Literature Review (SLR) process, applying the six stages of the review protocol from it, but added a previous stage, which aims to detect the need for a review. Then search equations were formulated and executed in several literature databases. Relevant publications were scanned and used to extract evidence to answer research questions. Results Several ML approaches have already been tested on other bioinformatics problems with promising results, yet there are few algorithms and architectures available in literature focused specifically on TEs, despite representing the majority of the nuclear DNA of many organisms. Only 35 articles were found and categorized as relevant in TE or related fields. Conclusions ML is a powerful tool that can be used to address many problems. Although ML techniques have been used widely in other biological tasks, their utilization in TE analyses is still limited. Following the SLR, it was possible to notice that the use of ML for TE analyses (detection and classification) is an open problem, and this new field of research is growing in interest.

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“…The strategy used was based on the guidelines proposed by (Wen et al, 2012) and used in (Franco-Bedoya et al, 2017) and (Tabares-Soto, Ramos-Pollan & Isaza, 2019). To define the need for a systematic review, we searched through published and available reviews (secondary studies) on the topic of interest.…”

Section: Identification Of the Need For A Reviewmentioning

confidence: 99%

Peer Review #1 of "A systematic review of the application of machine learning in the detection and classification of transposable elements (v0.1)"

2019

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Background. Transposable elements (TEs) constitute the most common repeated sequences in eukaryotic genomes. Recent studies demonstrated their deep impact on species diversity, adaptation to the environment, and diseases. Although there are many conventional bioinformatics algorithms for detecting and classifying TEs, none have achieved reliable results on different types of TEs. Machine learning (ML) techniques can automatically extract hidden patterns and novel information from labeled or non-labeled data and have been applied to solving several scientific problems. Methodology. We followed the Systematic Literature Review process, applying the six stages of the review protocol from it, but added a previous stage, which aims to detect the need for a review. Then search equations were formulated and executed in several literature databases. Relevant publications were scanned and used to extract evidence to answer research questions. Results. Several ML approaches have already been tested on other bioinformatics problems with promising results, yet there are few algorithms and architectures available in literature focused specifically on TEs, despite representing the majority of the nuclear DNA of many organisms. Only 35 papers were found and categorized as relevant in TE or related fields. Conclusions. ML is a powerful tool that can be used to address many problems. Although ML techniques have been used widely in other biological tasks, their utilization in TE analyses is still limited. Following the Systematic Literature Review process, it was possible to notice that the use of ML for TE analyses (detection and classification) is an open problem, and this new field of research is growing in interest.

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Deep Learning Applied to Steganalysis of Digital Images: A Systematic Review

Cited by 60 publications

References 54 publications

Steganography Techniques Using Convolutional Neural Networks

Steganography Techniques Using Convolutional Neural Networks

A systematic review of the application of machine learning in the detection and classification of transposable elements

Peer Review #1 of "A systematic review of the application of machine learning in the detection and classification of transposable elements (v0.1)"

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