A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Filters
Comprehensive Survey of Multimedia Steganalysis: Techniques, Evaluations, and Trends in Future Research
2022
Symmetry
Preserved Fulltext
In addition, it provides a deep review and summarizes recent steganalysis approaches and techniques for audio, images, and video. ...
In the modern world, digital multimedia such as audio, images, and video became popular and widespread, which makes them perfect candidates for steganography. ...
The authors, therefore, acknowledge with thanks DSR for technical and financial support.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/sym14010117
fatcat:s2oinfxtbjcdhjvdv7vqrs6jlm
Introduction to the special issue on deep learning for real-time information hiding and forensics
2020
Journal of Real-Time Image Processing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
The paper entitled "Deep learning for real-time image steganalysis: a survey", co-authored by Ruan et al. [16] , gives a survey on real-time image steganalysis based on deep learning. ...
Since deep learning techniques have gained great success in computer vision, many works also employ the deep learning techniques for image steganalysis to achieve higher accuracy and efficiency. ...
doi:10.1007/s11554-020-00947-2
fatcat:ge4olggcubhm3nouy4bkphezvu
AG-Net: An Advanced General CNN Model for Steganalysis
2022
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
In this paper, we propose an Advanced General convolutional neural Network (AG-Net) for steganalysis to deal with this problem. ...
Steganography has made great progress over the past few years due to the advancement of deep convolutional neural networks (DCNNs), which have been successfully used to multi-domains. ...
Aiming at the above problems of traditional steganalysis [4] , this paper combines deep learning with steganalysis and uses the deep learning model to obtain the simulation complex representation of the ...
doi:10.1109/access.2022.3150276
fatcat:eksatfdlmbettlc3mbnnbvy47m
Review on effectiveness of deep learning approach in digital forensics
2022
International Journal of Power Electronics and Drive Systems (IJPEDS)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Currently deep learning (DL), mainly convolutional neural network (CNN) has proved very promising in classification of digital images and sound analysis techniques. ...
There are several methods for digital forensic analysis. ...
However, this pipeline can be alternately implemented by a deep CNN that learns the optimized deep hierarchical representations for image steganalysis. ...
doi:10.11591/ijece.v12i5.pp5481-5592
fatcat:oyale5kuljcjvh54mkztfmcqde
Hierarchical Representation Network for Steganalysis of QIM Steganography in Low-Bit-Rate Speech Signals
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In this paper, motivated by the complex multi-scale structure, we design a Hierarchical Representation Network to tackle the steganalysis of QIM steganography in low-bit-rate speech signal. ...
Experiments demonstrated that the steganalysis performance of the proposed method can outperforms the state-of-the-art methods especially in detecting both short and low embeded speech samples. ...
Deep Learning Based steganalysis Method in VoIP Deep learning techniques have been well applied in image [23] and natural language processing [24] . ...
arXiv:1910.04433v1
fatcat:gtwmy7y3yna3dc3alos2vxdbyi
Constructing feature variation coefficients to evaluate feature learning capabilities of convolutional layers in steganographic detection algorithms of spatial domain
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
We select four typical image steganalysis models based CNN in spatial domain, such as Ye-Net, Yedroudj-Net, Zhu-Net, and SR-Net as use cases, and verify the validity of the variation coefficient through ...
Therefore, this paper proposes the variation coefficient to evaluate the feature learning ability of convolutional layers. ...
, fixed embedding algorithm,
fixed embedding rate
Deep Learning Hierarchical
Representations for Image
Steganalysis[13]
Network structure: 10-layer CNN network with
image preprocessing, increasing ...
arXiv:2010.10140v1
fatcat:ka4znn2xuvgcdmsgbydbnxzevq
CIS-Net: A Novel CNN Model for Spatial Image Steganalysis via Cover Image Suppression
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Several deep CNN models have been proposed via incorporating domain knowledge of image steganography/steganalysis into the design of the network and achieve state of the art performance on standard database ...
as possible in model learning. ...
Yi, “Deep learning hierarchical representations for
[7] B. Li, M. Wang, J. Huang, and X. ...
arXiv:1912.06540v1
fatcat:wiinc6uyo5hdldjicpmfwl6p6a
Review on Image Steganalysis Using INRIA Dataset
2018
Al-Nahrain Journal of Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
All these works depends on statistical properties of image. No one use machine learning tools like deep learning especially convolution neural network to detect attack in image using INRIA dataset. ...
This paper presents study on number of researches using INRIA dataset for image/ information retrieval and especially blind image steganalysis. ...
image steganalysis for a multi-classifier ...
doi:10.22401/anjs.21.4.13
fatcat:g2dkqfg4rbfh5g7iiop4yzahue
F3SNet: A Four-Step Strategy for QIM Steganalysis of Compressed Speech Based on Hierarchical Attention Network
2021
Security and Communication Networks
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
quantization index modulation steganalysis of compressed speech based on the hierarchical attention network. ...
Traditional machine learning-based steganalysis methods on compressed speech have achieved great success in the field of communication security. ...
In this paper, we introduce F3SNet, a four-step strategy for QIM steganalysis based on hierarchical encoding representations. ...
doi:10.1155/2021/1627486
fatcat:dtcrle2c4jg55miwzmjscrujd4
A Survey of Image Information Hiding Algorithms Based on Deep Learning
2018
CMES - Computer Modeling in Engineering & Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
At present, the model based on deep learning is also widely applied to the field of information hiding. This paper makes an overall conclusion on image information hiding based on deep learning. ...
It is divided into four parts of steganography algorithms, watermarking embedding algorithms, coverless information hiding algorithms and steganalysis algorithms based on deep learning. ...
[Zeng, Tan, Li et al. (2018) ] propose a general JPEG steganalysis framework for hybrid deep learning. ...
doi:10.31614/cmes.2018.04765
fatcat:tvmits2gdrb4xesfswtr275wpy
StegColNet: Steganalysis based on an ensemble colorspace approach
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Our results show that the proposed approach outperforms the recent state of the art deep learning steganalytical approaches by 2.32 percent on average for 0.2 bits per channel (bpc) and 1.87 percent on ...
Image steganography refers to the process of hiding information inside images. Steganalysis is the process of detecting a steganographic image. ...
Results comparison To compare our results, we considered three deep learning approaches for color steganalyzers, that are widely considered state of the art approaches: WISERNet [25] , Deep Hierarchical ...
arXiv:2002.02413v2
fatcat:iflbiwscdnaapaajyixqiiydli
Deep Learning Applied to Steganalysis of Digital Images: A Systematic Review
2019
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Likewise thanks to the project UN-UCALDAS Computational prototype for the fusion and analysis of large volumes of data in IoT (Internet of Things) environments, based on Machine Learning techniques and ...
Image taken from BOSSBase V1.01[12].
FIGURE 2 . 2 Steganalysis based on manual extraction of characteristics (top side) and steganalysis based on deep learning techniques (bottom side). ...
The general search string is listed below
((''Deep Learning'' OR ''Convolutional Neural Network'') AND (''Steganalysis'')) In Table 1 the databases and search strings used for the review are shown. ...
doi:10.1109/access.2019.2918086
fatcat:3o5mgkiyn5aj5ltdscr3cqr4by
Convolution Neural Networks for Blind Image Steganalysis: A Comprehensive Study
2019
Journal of Al-Qadisiyah for Computer Science and Mathematics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Long-standing and important problem in image steganalysis difficulties mainly lie in how to give high accuracy and low payload in stego or cover images for improving performance of the network. ...
In this comprehensive study a variety of scenarios and efforts are surveyed since 2014 at yet, in order to provide a guide to further improve future researchers what CNN-based blind image steganalysis ...
Introduction For solving difficult real-world problems quickly, deep learning techniques which is most important sub-field of machine learning has been used for classification and regression problems. ...
doi:10.29304/jqcm.2019.11.2.573
fatcat:rnwnn6q5lbc6xcoucqbmnhtyxm
Stegomalware: A Systematic Survey of MalwareHiding and Detection in Images, Machine LearningModels and Research Challenges
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
the Deep Learning(DL) models for hiding data detection. ...
Based on our findings, we perform the detail review of the image steganography techniques including the recent Generative Adversarial Networks (GAN) based models and the image steganalysis methods including ...
TABLE X DEEP X LEARNING STEGANALYSIS
TABLE XI DEEP XI LEARNING MODELS FOR IMAGE STEGANALYSIS PERFORMANCE
TABLE XII IMAGE XII DATASETS USED IN THE STATE OF THE ART FOR STEGANOGRAPHY AND STEGANALYSIS ...
arXiv:2110.02504v1
fatcat:wz5hnqeixrcdjc4afoyycjp7ui
STD-NET: Search of Image Steganalytic Deep-learning Architecture via Hierarchical Tensor Decomposition
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
In this paper, we propose STD-NET, an unsupervised deep-learning architecture search approach via hierarchical tensor decomposition for image steganalysis. ...
Recent studies shows that the majority of existing deep steganalysis models have a large amount of redundancy, which leads to a huge waste of storage and computing resources. ...
In [30] , Xu proposed a 20-layer deep residual steganalytic network. In [31] , Zeng et al. proposed a generic hybrid deep-learning framework aiming at large-scale JPEG image steganalysis. ...
arXiv:2206.05651v1
fatcat:zbkzxvcmjrao7ffuk2yqcnshu4
« Previous
Showing results 1 — 15 out of 131 results