A convolutional neural network-based linguistic steganalysis for synonym substitution steganography

Xiang, Ling Yun; Guo, Guo Qing; Yu, Jing; Sheng, Victor S.; Yang, Peng

doi:10.3934/mbe.2020055

Cited by 56 publications

(37 citation statements)

References 2 publications

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“…Our future work will focus on the development of a more robust BCI transfer learning algorithm that still has good performance and can be used online. We aim to mix our algorithm with some other algorithms [37][38][39] in future work. We believe that the transfer learning algorithm related to Riemannian Geometry has a promising future.…”

Section: Discussionmentioning

confidence: 99%

Transfer Learning Algorithm of P300-EEG Signal Based on XDAWN Spatial Filter and Riemannian Geometry Classifier

Xia

Wang

et al. 2020

Applied Sciences

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The electroencephalogram (EEG) signal in the brain–computer interface (BCI) has suffered great cross-subject variability. The BCI system needs to be retrained before each time it is used, which is a waste of resources and time. Thus, it is difficult to generalize a fixed classification method for all subjects. Therefore, the transfer learning method proposed in this article, which combines XDAWN spatial filter and Riemannian Geometry classifier (RGC), can achieve offline cross-subject transfer learning in the P300-speller paradigm. The XDAWN spatial filter is used to enhanced the P300 components in the raw signal as well as reduce its dimensions. Then, the Riemannian Geometry Mean (RGM) is used as the reference matrix to perform the affine transformation of the symmetric positive definite (SPD) covariance matrix calculated from the filtered signal, which makes the data from different subjects comparable. Finally, the RGC is used to obtain the result of transfer learning experiments. The proposed algorithm was evaluated on two datasets (Dataset I from real patients and Dataset II from the laboratory). By comparing with two state-of-the-art and classic algorithms in the current BCI field, Ensemble of Support Vector Machine (E-SVM) and Stepwise Linear Discriminant Analysis (SWLDA), the maximum averaged area under the receiver operating characteristic curve (AUC) score of our algorithm reached 0.836, proving the potential of our proposed algorithm.

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Section: Discussionmentioning

confidence: 99%

Transfer Learning Algorithm of P300-EEG Signal Based on XDAWN Spatial Filter and Riemannian Geometry Classifier

Xia

Wang

et al. 2020

Applied Sciences

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“…With the development of communication technology and the coming of information age, people are more and more aware of the important role of information security [71][72][73][74][75][76], and the research of various security protection has become the current research hotspot [77][78][79][80][81][82]. As an important part of information security transmission, random number generator (RNG) has been paid more and more attention.…”

Section: Introductionmentioning

confidence: 99%

Chaos-Based Application of a Novel Multistable 5D Memristive Hyperchaotic System with Coexisting Multiple Attractors

Liu

Qian

et al. 2020

Complexity

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Novel memristive hyperchaotic system designs and their engineering applications have received considerable critical attention. In this paper, a novel multistable 5D memristive hyperchaotic system and its application are introduced. The interesting aspect of this chaotic system is that it has different types of coexisting attractors, chaos, hyperchaos, periods, and limit cycles. First, a novel 5D memristive hyperchaotic system is proposed by introducing a flux-controlled memristor with quadratic nonlinearity into an existing 4D four-wing chaotic system as a feedback term. Then, the phase portraits, Lyapunov exponential spectrum, bifurcation diagram, and spectral entropy are used to analyze the basic dynamics of the 5D memristive hyperchaotic system. For a specific set of parameters, we find an unusual metastability, which shows the transition from chaotic to periodic (period-2 and period-3) dynamics. Moreover, its circuit implementation is also proposed. By using the chaoticity of the novel hyperchaotic system, we have developed a random number generator (RNG) for practical image encryption applications. Furthermore, security analyses are carried out with the RNG and image encryption designs.

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“…As the steganography techniques could be maliciously used for stealing confidential information, it is practically significant to carry on researches to forensics of the crime. For decades, researchers have proposed many techniques for the forensics of steganography, including the stego detection [2][3][4][5][6][7][8][9][10], the payload location [11][12][13][14][15], the embedding key restore [16,17], the secret message extraction [17], and the steganographer detection [18][19][20]. In practice, the covert communication entity on the Internet usually acts as the user of social platforms, whose location is virtual.…”

Section: Introductionmentioning

confidence: 99%

Geolocation of covert communication entity on the Internet for post-steganalysis

Zhang

Liu

Luo

2020

J Image Video Proc.

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Geolocation of covert communication entity is significantly important for the forensics of the crime but has significant challenges when the steganalyst locks the guilty actor IP and wants to know the physical location of the actor. This kind of post-steganalysis involves not only the stegos transmitted on the Internet but the IP package head and content. This paper presents a geolocation method for the location of the covert communication entity based on hophot path coding. The method estimates the location of the covert communication entity by combining the path and delay between probes and the covert communication entity IP, which improves the deficiency that similar delays do not necessarily mean close geographical locations of the IPs. Moreover, the similarity between the IPs' paths can be measured by coding the paths between IPs and probes. The results of a series of experiments show that the median error of the proposed method is within 6.16 km using different thresholds.

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A convolutional neural network-based linguistic steganalysis for synonym substitution steganography

Cited by 56 publications

References 2 publications

Transfer Learning Algorithm of P300-EEG Signal Based on XDAWN Spatial Filter and Riemannian Geometry Classifier

Transfer Learning Algorithm of P300-EEG Signal Based on XDAWN Spatial Filter and Riemannian Geometry Classifier

Chaos-Based Application of a Novel Multistable 5D Memristive Hyperchaotic System with Coexisting Multiple Attractors

Geolocation of covert communication entity on the Internet for post-steganalysis

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