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Multi-frequency Residual Convolutional Neural Network for Steganalysis of Color Images
2021
IEEE Access
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Therefore, in order to ensure the practicability of the designed steganalysis algorithm, it is usually necessary to consider the influence of the mismatch of the steganalysis algorithm and the mismatch ...
According to its embedding principle, it can be divided into steganography algorithm in the spatial domain and steganography algorithm in the frequency domain. ...
doi:10.1109/access.2021.3119664
fatcat:a5e7abjuync45cwouhxxlhnuhu
A Survey on Deep Convolutional Neural Networks for Image Steganography and Steganalysis
2020
KSII Transactions on Internet and Information Systems
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In this paper, we analyzed current research states from the latest image steganography and steganalysis frameworks based on deep learning. ...
Steganalysis & steganography have witnessed immense progress over the past few years by the advancement of deep convolutional neural networks (DCNN). ...
Cover Source Mismatch (CSM) or Stego Mismatch: Cover source mismatch problems take place when the steganalysis detector is trained on one dataset and test on different dataset. ...
doi:10.3837/tiis.2020.03.017
fatcat:7ci7bfbjsfd2nn5yagnv2h3ora
Deep Learning in steganography and steganalysis from 2015 to 2018
[article]
2019
arXiv
pre-print
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Between 2015-2018, numerous publications have shown that it is possible to obtain improved performances, notably in spatial steganalysis, JPEG steganalysis, Selection-Channel-Aware steganalysis, and in ...
So, we will present the structure of a deep neural network, in a generic way and present the networks proposed in existing literature for the different scenarios of steganalysis, and finally, we will discuss ...
THE SPATIAL STEGANALYSIS NOT-SIDE-CHANNEL-AWARE (NOT-SCA) In early 2018 the most successful spatial steganalysis approach is the Yedroudj-Net [108] method (See Figure 1.9) . ...
arXiv:1904.01444v2
fatcat:z7lxk34zh5bdplopwfbwztfdce
IStego100K: Large-scale Image Steganalysis Dataset
[article]
2019
arXiv
pre-print
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In addition, we hope that IStego100K can help researchers further explore the development of universal image steganalysis algorithms, so we try to reduce limits on the images in IStego100K. ...
We tested the performance of some latest steganalysis algorithms on IStego100K, with specific results and analysis details in the experimental part. ...
We believe that in order to achieve more practical and general steganalysis algorithm, the problem of sample source mismatch is worth considering. ...
arXiv:1911.05542v1
fatcat:ffcmw46zwvg45l5ovbvfaxtjf4
Deep Learning Applied to Steganalysis of Digital Images: A Systematic Review
2019
IEEE Access
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They accompanied us in the process of writing and review, also we shared with these professors long talks about this topic. ...
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 ...
experiments and study more deeply the Cover-Source Mismatch effect. • Perform steganalysis by testing more steganographic algorithms in the JPEG domain. • Adapt the GAN methodology to do steganalysis ...
doi:10.1109/access.2019.2918086
fatcat:3o5mgkiyn5aj5ltdscr3cqr4by
A Novel Convolutional Neural Network for Image Steganalysis with Shared Normalization
[article]
2017
arXiv
pre-print
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In this paper, we explore an important technique in deep learning, the batch normalization, for the task of image steganalysis. ...
Deep learning based image steganalysis has attracted increasing attentions in recent years. ...
In mismatched case, the network is trained by S-UNIWARD steganography but tested by other three steganographic algorithms. ...
arXiv:1711.07306v2
fatcat:3te6l2mywfeo3dvtl4ngua6xo4
Using contrastive learning to improve the performance of steganalysis schemes
[article]
2021
arXiv
pre-print
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To decrease the computing complexity of the contrastive loss in supervised learning, we design a novel Steganalysis Contrastive Loss (StegCL) based on the equivalence and transitivity of similarity. ...
The StegCL eliminates the redundant computing in the existing contrastive loss. ...
The of KeNet [24] and CL-KeNet in case of mismatch is shown in Table 2 and Table 3 . Table 2 shows the in case of steganography algorithm mismatch. ...
arXiv:2103.00891v1
fatcat:2lggtp2q4faabnmvama3dmczym
Deep learning is a good steganalysis tool when embedding key is reused for different images, even if there is a cover sourcemismatch
2016
IS&T International Symposium on Electronic Imaging Science and Technology
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The results show more than 16% reduction in the classification error with our CNN or FNN. Third, experiments were also performed in a cover-source mismatch setting. ...
In 2015, Qian et al. have shown that the use of a deep learning approach that jointly learns and computes the features, was very promising for the steganalysis. ...
With the knowledge of this spatial pattern of probability of change it becomes easier to apply a classical steganalysis, as in the present article, or a forensic-steganalysis such as payload localization ...
doi:10.2352/issn.2470-1173.2016.8.mwsf-078
fatcat:vdmmyamd6rdjdkeso6yptwj3au
Deep learning is a good steganalysis tool when embedding key is reused for different images, even if there is a cover source-mismatch
[article]
2018
arXiv
pre-print
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The results show more than 16% reduction in the classification error with our CNN or FNN. Third, experiments were also performed in a cover-source mismatch setting. ...
In 2015, Qian et al. have shown that the use of a deep learning approach that jointly learns and computes the features, is very promising for the steganalysis. ...
With the knowledge of this spatial pattern of probability of change it becomes easier to apply a classical steganalysis, as in the present article, or a forensic-steganalysis such as payload localization ...
arXiv:1511.04855v2
fatcat:ikxywmpqaze5hbjpmunrs4tf3u
MI-STEG: A Medical Image Steganalysis Framework Based on Ensemble Deep Learning
2023
Computers Materials & Continua
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Spatial Version of the Universal Wavelet Relative Distortion (S-UNIWARD), Highly Undetectable Stego (HUGO), and Minimizing the Power of Optimal Detector (MIPOD) techniques used in spatial image steganalysis ...
The study demonstrated the success of pre-trained ResNet, DenseNet, and Inception models in the cover-stego mismatch scenario for each hiding technique with different payloads. ...
Burhanettin Cigdem from Cumhuriyet University, Sivas, Turkey, for sharing the data used in this article. ...
doi:10.32604/cmc.2023.035881
fatcat:ovp4wsfkkjandaf2lgrzk24s3y
Graph Representation Learning for Spatial Image Steganalysis
[article]
2022
arXiv
pre-print
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In this paper, we introduce a graph representation learning architecture for spatial image steganalysis, which is motivated by the assumption that steganographic modifications unavoidably distort the statistical ...
By feeding the graph to an attention network, the discriminative features can be learned for efficient steganalysis. ...
CONCLUSION This paper presents a general graph learning framework for spatial steganalysis. ...
arXiv:2110.00957v3
fatcat:7tec4xvcrfapvnqcumlja55ryi
Constructing feature variation coefficients to evaluate feature learning capabilities of convolutional layers in steganographic detection algorithms of spatial domain
[article]
2020
arXiv
pre-print
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Traditional steganalysis methods generally include two steps: feature extraction and classification.A variety of steganalysis algorithms based on CNN (Convolutional Neural Network) have appeared in recent ...
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 ...
Common performance indicators In the field of spatial domain steganalysis technology research, the traditional indicators to measure the performance of certain steganalysis algorithms are usually as follows ...
arXiv:2010.10140v1
fatcat:ka4znn2xuvgcdmsgbydbnxzevq
A Siamese Inverted Residuals Network Image Steganalysis Scheme based on Deep Learning
2023
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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is easily affected by cover mismatch. ...
In this manuscript, we propose a steganalysis method based on Inverse Residuals structured Siamese network (abbreviated as SiaIRNet method, Sia mese- I nverted- R esiduals- Net work Based method). ...
Cover Mismatch and Algorithm Mismatch Image Detection In steganalysis methods, there will be situations where the training image set and the test image set come from diferent sources. ...
doi:10.1145/3579166
fatcat:o3hkf4r4j5dvflbnqqgjmzz6qe
IAS-CNN: Image adaptive steganalysis via convolutional neural network combined with selection channel
2020
International Journal of Distributed Sensor Networks
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Nowadays, steganalysis based on deep learning generally has a large number of parameters, and its pertinence to adaptive steganography algorithms is weak. ...
Experimental results show that IAS-CNN performs well in steganalysis. ...
In order to detect adaptive steganography algorithms, steganalysis algorithms using higher order statistical features have been proposed, such as spatial rich model (SRM) 5 and several models 6,7 based ...
doi:10.1177/1550147720911002
fatcat:cqpbw4h6fzdqteu7chlwgewhmm
"Break Our Steganographic System": The Ins and Outs of Organizing BOSS
[chapter]
2011
Lecture Notes in Computer Science
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We also point to other practical issues related to designing steganographic systems and give several suggestions for future contests in steganalysis. ...
We explain the motivations behind the organization of the contest, its rules together with reasons for them, and the steganographic algorithm developed for the contest. ...
HUGO, the embedding algorithm for BOSS The HUGO (Highly Undetectable steGO) algorithm used in the contest hides messages into least significant bits of grayscale images represented in the spatial domain ...
doi:10.1007/978-3-642-24178-9_5
fatcat:pizrqckyyfgu7gfvlmairzf6vi
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