Improving Selection-Channel-Aware Steganalysis Features

Denemark, Tomáš; Fridrich, Jessica; Comesana-Alfaro, Pedro

doi:10.2352/issn.2470-1173.2016.8.mwsf-080

Cited by 46 publications

(19 citation statements)

References 26 publications

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“…However, most deep learning researches adopt the 2D network, which ignores the fact of multi-channel signal processing [ 40 ], Table 6 shows that the more channels EEG signal could improve the performance of the network. We proposed the 3D image reconstruction approach to relate multi-channel information, just like in video processing [ 41 ].…”

Section: Discussionmentioning

confidence: 99%

Automatic seizure detection using three-dimensional CNN based on multi-channel EEG

Wei

Zhou

Chen

et al. 2018

BMC Med Inform Decis Mak

109

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BackgroundAutomated seizure detection from clinical EEG data can reduce the diagnosis time and facilitate targeting treatment for epileptic patients. However, current detection approaches mainly rely on limited features manually designed by domain experts, which are inflexible for the detection of a variety of patterns in a large amount of patients’ EEG data. Moreover, conventional machine learning algorithms for seizure detection cannot accommodate multi-channel Electroencephalogram (EEG) data effectively, which contains both temporal and spatial information. Recently, deep learning technology has been widely applied to perform image processing tasks, which could learns useful features from data and process multi-channel data automatically. To provide an effective system for automatic seizure detection, we proposed a new three-dimensional (3D) convolutional neural network (CNN) structure, whose inputs are multi-channel EEG signals.MethodsEEG data of 13 patients were collected from one center hospital, which has already been inspected by experts. To represent EEG data in CNN, firstly time series of each channel of EEG data was converted into the two-dimensional image. Then all channel images were combined into 3D images according to the mutual correlation intensity between different electrodes. Finally, a CNN was constructed using 3D kernels to predict different stages of EEG data, including inter-ictal, pre-ictal, and ictal stages. The system performance was evaluated and compared with the traditional feature-based classifier and the two-dimensional (2D) deep learning method.ResultsIt demonstrated that multi-channel EEG data could provide more information for increasing the specificity and sensitivity in cpmparison result between the single and multi-channel. And the 3D CNN based on multi-channel outperformed the 2D CNN and traditional signal processing methods with an accuracy of more than 90%, an sensitivity of 88.90% and an specificity of 93.78%.ConclusionsThis is the first effort to apply 3D CNN in detecting seizures from EEG. It provides a new way of learning patterns simultaneously from multi-channel EEG signals, and demonstrates that deep neural networks in combination with 3D kernels can establish an effective system for seizure detection.

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Section: Discussionmentioning

confidence: 99%

Automatic seizure detection using three-dimensional CNN based on multi-channel EEG

Wei

Zhou

Chen

et al. 2018

BMC Med Inform Decis Mak

109

View full text Add to dashboard Cite

show abstract

“…In the consideration of selection-cannel, Denemark el al. [Denemark, Fridrich and Comesaña-Alfaro (2016); Denemark, Boroumand and Fridrich 2016] proposed algorithms those extracting features of images in independent cannel. In addition to traditional methods based on artificial features, steganalysis methods based on deep learning have been further developed.…”

Section: Steganalysismentioning

confidence: 99%

A Survey of Image Information Hiding Algorithms Based on Deep Learning

Meng¹,

Cui²,

Yuan³

2018

CMES

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With the development of data science and technology, information security has been further concerned. In order to solve privacy problems such as personal privacy being peeped and copyright being infringed, information hiding algorithms has been developed. Image information hiding is to make use of the redundancy of the cover image to hide secret information in it. Ensuring that the stego image cannot be distinguished from the cover image, and sending secret information to receiver through the transmission of the stego image. 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. From these four aspects, the state-of-the-art information hiding technologies based on deep learning are illustrated and analyzed.

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“…As an illustration, for S-UNIWARD they observed detection accuracies of 57.33% and 80.24% for payloads of 0.1 and 0.4 bpp, whereas the ones of SRM+EC were, respectively, of 59.25% and 79.53%. However, the authors have not applied their approach to JPEG domain steganographic algorithms and they pointed out that in the spatial domain more advanced conventional steganalysis methods, such as [24] or [25], still outperformed their approach.…”

Section: B Cnn-based Steganalysismentioning

confidence: 99%

Improving Blind Steganalysis in Spatial Domain Using a Criterion to Choose the Appropriate Steganalyzer Between CNN and SRM+EC

Couchot

Couturier

Salomon

2017

ICT Systems Security and Privacy Protection

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Part 6: Applied Cryptography and Voting SchemesInternational audienceConventional state-of-the-art image steganalysis approaches usually consist of a classifier trained with features provided by rich image models. As both features extraction and classification steps are perfectly embodied in the deep learning architecture called Convolutional Neural Network (CNN), different studies have tried to design a CNN-based steganalyzer. This work proposes a criterion to choose either the CNN designed by Xu et al. or the combination Spatial Rich Models (SRM) and Ensemble Classifier (EC) for an input image. Our approach is studied with three steganographic spatial domain algorithms: S-UNIWARD, MiPOD, and HILL, and exhibits detection capabilities better than each method alone. As SRM+EC and the CNN are only trained with MiPOD the proposed method can be seen as an approach for blind steganalysis

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Improving Selection-Channel-Aware Steganalysis Features

Cited by 46 publications

References 26 publications

Automatic seizure detection using three-dimensional CNN based on multi-channel EEG

Automatic seizure detection using three-dimensional CNN based on multi-channel EEG

A Survey of Image Information Hiding Algorithms Based on Deep Learning

Improving Blind Steganalysis in Spatial Domain Using a Criterion to Choose the Appropriate Steganalyzer Between CNN and SRM+EC

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